Overview

Dataset statistics

Number of variables39
Number of observations1539
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory469.0 KiB
Average record size in memory312.1 B

Variable types

Categorical22
Numeric17

Alerts

title has a high cardinality: 1201 distinct values High cardinality
title_orig has a high cardinality: 1203 distinct values High cardinality
tags has a high cardinality: 1230 distinct values High cardinality
product_color has a high cardinality: 102 distinct values High cardinality
product_variation_size_id has a high cardinality: 107 distinct values High cardinality
merchant_title has a high cardinality: 958 distinct values High cardinality
merchant_name has a high cardinality: 958 distinct values High cardinality
merchant_info_subtitle has a high cardinality: 1059 distinct values High cardinality
merchant_id has a high cardinality: 958 distinct values High cardinality
product_url has a high cardinality: 1341 distinct values High cardinality
product_picture has a high cardinality: 1341 distinct values High cardinality
product_id has a high cardinality: 1341 distinct values High cardinality
tag_list has a high cardinality: 1230 distinct values High cardinality
price is highly correlated with retail_price and 1 other fieldsHigh correlation
retail_price is highly correlated with priceHigh correlation
units_sold is highly correlated with rating_count and 5 other fieldsHigh correlation
rating_count is highly correlated with units_sold and 5 other fieldsHigh correlation
rating_five_count is highly correlated with units_sold and 5 other fieldsHigh correlation
rating_four_count is highly correlated with units_sold and 5 other fieldsHigh correlation
rating_three_count is highly correlated with units_sold and 5 other fieldsHigh correlation
rating_two_count is highly correlated with units_sold and 5 other fieldsHigh correlation
rating_one_count is highly correlated with units_sold and 5 other fieldsHigh correlation
badges_count is highly correlated with badge_product_qualityHigh correlation
badge_product_quality is highly correlated with badges_countHigh correlation
shipping_option_price is highly correlated with priceHigh correlation
price is highly correlated with shipping_option_priceHigh correlation
units_sold is highly correlated with rating_count and 5 other fieldsHigh correlation
rating_count is highly correlated with units_sold and 5 other fieldsHigh correlation
rating_five_count is highly correlated with units_sold and 5 other fieldsHigh correlation
rating_four_count is highly correlated with units_sold and 5 other fieldsHigh correlation
rating_three_count is highly correlated with units_sold and 5 other fieldsHigh correlation
rating_two_count is highly correlated with units_sold and 5 other fieldsHigh correlation
rating_one_count is highly correlated with units_sold and 5 other fieldsHigh correlation
badges_count is highly correlated with badge_local_product and 1 other fieldsHigh correlation
badge_local_product is highly correlated with badges_countHigh correlation
badge_product_quality is highly correlated with badges_countHigh correlation
shipping_option_price is highly correlated with priceHigh correlation
price is highly correlated with shipping_option_priceHigh correlation
units_sold is highly correlated with rating_count and 5 other fieldsHigh correlation
rating_count is highly correlated with units_sold and 5 other fieldsHigh correlation
rating_five_count is highly correlated with units_sold and 5 other fieldsHigh correlation
rating_four_count is highly correlated with units_sold and 5 other fieldsHigh correlation
rating_three_count is highly correlated with units_sold and 5 other fieldsHigh correlation
rating_two_count is highly correlated with units_sold and 5 other fieldsHigh correlation
rating_one_count is highly correlated with units_sold and 5 other fieldsHigh correlation
badges_count is highly correlated with badge_product_qualityHigh correlation
badge_product_quality is highly correlated with badges_countHigh correlation
shipping_option_price is highly correlated with priceHigh correlation
badges_count is highly correlated with badge_local_product and 3 other fieldsHigh correlation
badge_local_product is highly correlated with badges_countHigh correlation
shipping_is_express is highly correlated with badges_countHigh correlation
badge_product_quality is highly correlated with badges_countHigh correlation
badge_fast_shipping is highly correlated with badges_countHigh correlation
price is highly correlated with shipping_option_priceHigh correlation
units_sold is highly correlated with rating_count and 5 other fieldsHigh correlation
rating is highly correlated with badge_product_qualityHigh correlation
rating_count is highly correlated with units_sold and 5 other fieldsHigh correlation
rating_five_count is highly correlated with units_sold and 5 other fieldsHigh correlation
rating_four_count is highly correlated with units_sold and 5 other fieldsHigh correlation
rating_three_count is highly correlated with units_sold and 5 other fieldsHigh correlation
rating_two_count is highly correlated with units_sold and 5 other fieldsHigh correlation
rating_one_count is highly correlated with units_sold and 6 other fieldsHigh correlation
badges_count is highly correlated with badge_local_product and 3 other fieldsHigh correlation
badge_local_product is highly correlated with badges_count and 1 other fieldsHigh correlation
badge_product_quality is highly correlated with rating and 1 other fieldsHigh correlation
badge_fast_shipping is highly correlated with badges_count and 2 other fieldsHigh correlation
shipping_option_price is highly correlated with price and 1 other fieldsHigh correlation
shipping_is_express is highly correlated with badges_count and 2 other fieldsHigh correlation
origin_country is highly correlated with merchant_ratingHigh correlation
merchant_rating_count is highly correlated with rating_one_countHigh correlation
merchant_rating is highly correlated with origin_countryHigh correlation
title is uniformly distributed Uniform
title_orig is uniformly distributed Uniform
tags is uniformly distributed Uniform
merchant_info_subtitle is uniformly distributed Uniform
product_url is uniformly distributed Uniform
product_picture is uniformly distributed Uniform
product_id is uniformly distributed Uniform
tag_list is uniformly distributed Uniform
rating_count has 43 (2.8%) zeros Zeros
rating_five_count has 70 (4.5%) zeros Zeros
rating_four_count has 130 (8.4%) zeros Zeros
rating_three_count has 130 (8.4%) zeros Zeros
rating_two_count has 228 (14.8%) zeros Zeros
rating_one_count has 152 (9.9%) zeros Zeros

Reproduction

Analysis started2022-08-24 23:40:51.529044
Analysis finished2022-08-24 23:41:47.637648
Duration56.11 seconds
Software versionpandas-profiling v3.2.0
Download configurationconfig.json

Variables

title
Categorical

HIGH CARDINALITY
UNIFORM

Distinct1201
Distinct (%)78.0%
Missing0
Missing (%)0.0%
Memory size12.1 KiB
Nouvelle mode d'été femmes robe décontractée col rond lâche Big Swing jupe sans manches Soild couleur robe de plage
 
22
Mini robe de soirée décontractée sans manches pour femmes
 
11
Pantalon à lacets à la mode pour femmes d'été, plus la taille Pantalon court à taille haute décontracté
 
9
Tissu taille formateur gilet chaud shaper été shaperwear minceur réglable sueur ceinture corps shaper
 
9
Femmes d'été Sling Dress V-cou Floral Strap plissé Casual Pocket Large Dress
 
9
Other values (1196)
1479 

Length

Max length327
Median length194
Mean length117.0116959
Min length27

Characters and Unicode

Total characters180081
Distinct characters99
Distinct categories12 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique996 ?
Unique (%)64.7%

Sample

1st row2020 Summer Vintage Flamingo Print Pajamas Set Casual Loose T Shirt Top And Elastic Shorts Women Sleepwear Night Wear Loungewear Sets
2nd rowSSHOUSE Summer Casual Sleeveless Soirée Party Soirée sans manches Vêtements de plage sexy Mini robe femme wshC1612242400387A21
3rd row2020 Nouvelle Arrivée Femmes Printemps et Été Plage Porter Longue Mince Cardigan Ouvert Avant Kimono Vert Feuille Imprimé En Mousseline de Soie Cardigan S-5XL
4th rowHot Summer Cool T-shirt pour les femmes Mode Tops Abeille Lettres imprimées Manches courtes O Neck Coton T-shirts Tops Tee Vêtements
5th rowFemmes Shorts d'été à lacets taille élastique lâche mince pantalon décontracté, plus la taille S-8XL

Common Values

ValueCountFrequency (%)
Nouvelle mode d'été femmes robe décontractée col rond lâche Big Swing jupe sans manches Soild couleur robe de plage22
 
1.4%
Mini robe de soirée décontractée sans manches pour femmes11
 
0.7%
Pantalon à lacets à la mode pour femmes d'été, plus la taille Pantalon court à taille haute décontracté9
 
0.6%
Tissu taille formateur gilet chaud shaper été shaperwear minceur réglable sueur ceinture corps shaper9
 
0.6%
Femmes d'été Sling Dress V-cou Floral Strap plissé Casual Pocket Large Dress9
 
0.6%
Pantalon de mode d'été Femmes Leggings Pantalon déchiré Pantalon slim Pantalon vert armée Collants7
 
0.5%
Mode féminine été bretelles spaghetti imprimé floral nouer devant mini robe robe sexy7
 
0.5%
Femmes été décontracté lâche couleur unie salopette vintage sangle pantalon long combinaisons barboteuses grande taille6
 
0.4%
Mode féminine Maillots de bain Deux pièces Split Bikini Set Summer Printed Beach Bathing Swimsuits6
 
0.4%
Femmes Shorts d'été à lacets taille élastique lâche mince pantalon décontracté, plus la taille S-8XL6
 
0.4%
Other values (1191)1447
94.0%

Length

2022-08-25T01:41:47.794661image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
femmes979
 
3.4%
de896
 
3.1%
robe869
 
3.0%
manches754
 
2.6%
d'été736
 
2.6%
mode630
 
2.2%
taille592
 
2.1%
à574
 
2.0%
sans510
 
1.8%
casual436
 
1.5%
Other values (1937)21539
75.5%

Most occurring characters

ValueCountFrequency (%)
27101
15.0%
e19210
 
10.7%
s10930
 
6.1%
o10209
 
5.7%
a9604
 
5.3%
l8355
 
4.6%
t8254
 
4.6%
r7997
 
4.4%
n7850
 
4.4%
i7503
 
4.2%
Other values (89)63068
35.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter130919
72.7%
Space Separator27104
 
15.1%
Uppercase Letter18315
 
10.2%
Decimal Number1452
 
0.8%
Other Punctuation1121
 
0.6%
Dash Punctuation1019
 
0.6%
Close Punctuation57
 
< 0.1%
Open Punctuation57
 
< 0.1%
Math Symbol26
 
< 0.1%
Connector Punctuation8
 
< 0.1%
Other values (2)3
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e19210
14.7%
s10930
 
8.3%
o10209
 
7.8%
a9604
 
7.3%
l8355
 
6.4%
t8254
 
6.3%
r7997
 
6.1%
n7850
 
6.0%
i7503
 
5.7%
m6791
 
5.2%
Other values (26)34216
26.1%
Uppercase Letter
ValueCountFrequency (%)
S2901
15.8%
C1771
9.7%
T1437
 
7.8%
F1300
 
7.1%
D1290
 
7.0%
P1265
 
6.9%
M1178
 
6.4%
L1177
 
6.4%
B893
 
4.9%
R845
 
4.6%
Other values (18)4258
23.2%
Other Punctuation
ValueCountFrequency (%)
'890
79.4%
,135
 
12.0%
/39
 
3.5%
"24
 
2.1%
.13
 
1.2%
:8
 
0.7%
!4
 
0.4%
&4
 
0.4%
\2
 
0.2%
%1
 
0.1%
Decimal Number
ValueCountFrequency (%)
2334
23.0%
0322
22.2%
5242
16.7%
1173
11.9%
8110
 
7.6%
988
 
6.1%
368
 
4.7%
447
 
3.2%
645
 
3.1%
723
 
1.6%
Close Punctuation
ValueCountFrequency (%)
)53
93.0%
3
 
5.3%
]1
 
1.8%
Open Punctuation
ValueCountFrequency (%)
(50
87.7%
6
 
10.5%
[1
 
1.8%
Space Separator
ValueCountFrequency (%)
27101
> 99.9%
 3
 
< 0.1%
Math Symbol
ValueCountFrequency (%)
~15
57.7%
+11
42.3%
Dash Punctuation
ValueCountFrequency (%)
-1019
100.0%
Connector Punctuation
ValueCountFrequency (%)
_8
100.0%
Modifier Symbol
ValueCountFrequency (%)
`2
100.0%
Control
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin149234
82.9%
Common30847
 
17.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e19210
 
12.9%
s10930
 
7.3%
o10209
 
6.8%
a9604
 
6.4%
l8355
 
5.6%
t8254
 
5.5%
r7997
 
5.4%
n7850
 
5.3%
i7503
 
5.0%
m6791
 
4.6%
Other values (54)52531
35.2%
Common
ValueCountFrequency (%)
27101
87.9%
-1019
 
3.3%
'890
 
2.9%
2334
 
1.1%
0322
 
1.0%
5242
 
0.8%
1173
 
0.6%
,135
 
0.4%
8110
 
0.4%
988
 
0.3%
Other values (25)433
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII174658
97.0%
None5423
 
3.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
27101
15.5%
e19210
 
11.0%
s10930
 
6.3%
o10209
 
5.8%
a9604
 
5.5%
l8355
 
4.8%
t8254
 
4.7%
r7997
 
4.6%
n7850
 
4.5%
i7503
 
4.3%
Other values (74)57645
33.0%
None
ValueCountFrequency (%)
é4015
74.0%
à501
 
9.2%
â296
 
5.5%
É221
 
4.1%
è136
 
2.5%
ê112
 
2.1%
À74
 
1.4%
ô27
 
0.5%
î16
 
0.3%
6
 
0.1%
Other values (5)19
 
0.4%

title_orig
Categorical

HIGH CARDINALITY
UNIFORM

Distinct1203
Distinct (%)78.2%
Missing0
Missing (%)0.0%
Memory size12.1 KiB
New Fashion Summer Women Casual Dress Round Neck Loose Big Swing Skirt Sleeveless Soild Color Beach dress
 
22
Sexy Women's Summer Casual Sleeveless Evening Party Backless Beachwear Mini Dress
 
11
Summer Women Sling Dress V-neck Floral Pleated Strap Casual Pocket Large Dress
 
9
Summer Women s Fashion Lace Up Tie Pants Plus Size Casual High Waist Short Pants
 
9
Fabric Waist Trainer Vest Hot Shaper Summer Shaperwear Slimming Adjustable Sweat Belt Body Shaper
 
9
Other values (1198)
1479 

Length

Max length272
Median length177
Mean length102.668616
Min length21

Characters and Unicode

Total characters158007
Distinct characters99
Distinct categories14 ?
Distinct scripts2 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique998 ?
Unique (%)64.8%

Sample

1st row2020 Summer Vintage Flamingo Print Pajamas Set Casual Loose T Shirt Top And Elastic Shorts Women Sleepwear Night Wear Loungewear Sets
2nd rowWomen's Casual Summer Sleeveless Sexy Mini Dress
3rd row2020 New Arrival Women Spring and Summer Beach Wear Long Thin Cardigan Open Front Kimono Green Leaf Printed Chiffon Cardigan S-5XL
4th rowHot Summer Cool T Shirt for Women Fashion Tops Bee Printed Letters Short Sleeve O Neck Cotton T-shirts Tops Tee Clothing
5th rowWomen Summer Shorts Lace Up Elastic Waistband Loose Thin Casual Pants Plus Size S-8XL

Common Values

ValueCountFrequency (%)
New Fashion Summer Women Casual Dress Round Neck Loose Big Swing Skirt Sleeveless Soild Color Beach dress22
 
1.4%
Sexy Women's Summer Casual Sleeveless Evening Party Backless Beachwear Mini Dress11
 
0.7%
Summer Women Sling Dress V-neck Floral Pleated Strap Casual Pocket Large Dress9
 
0.6%
Summer Women s Fashion Lace Up Tie Pants Plus Size Casual High Waist Short Pants9
 
0.6%
Fabric Waist Trainer Vest Hot Shaper Summer Shaperwear Slimming Adjustable Sweat Belt Body Shaper9
 
0.6%
Women's Summer Fashion Spaghetti Strap Floral Print Tie Front Mini Dress Sexy Dress7
 
0.5%
Summer Fashion Trousers Women Leggings Ripped Pants Slim Pants Army Green Tights Pants7
 
0.5%
Women Fashion Swimwear Two Pieces Split Bikini Set Summer Printed Beach Bathing Swimsuits6
 
0.4%
Women Summer Casual Loose Solid Color Vintage Overalls Strap Long Pants Jumpsuits Rompers Plus Size6
 
0.4%
Women Summer Shorts Lace Up Elastic Waistband Loose Thin Casual Pants Plus Size S-8XL6
 
0.4%
Other values (1193)1447
94.0%

Length

2022-08-25T01:41:48.012133image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
summer1292
 
5.2%
dress1005
 
4.1%
women989
 
4.0%
fashion846
 
3.4%
casual825
 
3.3%
size583
 
2.4%
plus550
 
2.2%
sleeveless528
 
2.1%
loose440
 
1.8%
short420
 
1.7%
Other values (1509)17268
69.8%

Most occurring characters

ValueCountFrequency (%)
23452
14.8%
e15434
 
9.8%
s11399
 
7.2%
o9266
 
5.9%
r7925
 
5.0%
a7571
 
4.8%
i7425
 
4.7%
n6791
 
4.3%
S6605
 
4.2%
t6395
 
4.0%
Other values (89)55744
35.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter106336
67.3%
Uppercase Letter25138
 
15.9%
Space Separator23452
 
14.8%
Decimal Number1410
 
0.9%
Dash Punctuation937
 
0.6%
Other Punctuation565
 
0.4%
Close Punctuation56
 
< 0.1%
Open Punctuation56
 
< 0.1%
Math Symbol26
 
< 0.1%
Final Punctuation13
 
< 0.1%
Other values (4)18
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e15434
14.5%
s11399
10.7%
o9266
8.7%
r7925
 
7.5%
a7571
 
7.1%
i7425
 
7.0%
n6791
 
6.4%
t6395
 
6.0%
l6277
 
5.9%
m5189
 
4.9%
Other values (21)22664
21.3%
Uppercase Letter
ValueCountFrequency (%)
S6605
26.3%
C2005
 
8.0%
P1994
 
7.9%
W1855
 
7.4%
T1832
 
7.3%
L1699
 
6.8%
D1478
 
5.9%
F1440
 
5.7%
B1400
 
5.6%
N727
 
2.9%
Other values (18)4103
16.3%
Other Punctuation
ValueCountFrequency (%)
'430
76.1%
/40
 
7.1%
,34
 
6.0%
"18
 
3.2%
&15
 
2.7%
.7
 
1.2%
:7
 
1.2%
;4
 
0.7%
!4
 
0.7%
#2
 
0.4%
Other values (3)4
 
0.7%
Decimal Number
ValueCountFrequency (%)
2322
22.8%
0312
22.1%
5239
17.0%
1162
11.5%
8105
 
7.4%
987
 
6.2%
369
 
4.9%
449
 
3.5%
644
 
3.1%
721
 
1.5%
Close Punctuation
ValueCountFrequency (%)
)52
92.9%
3
 
5.4%
]1
 
1.8%
Open Punctuation
ValueCountFrequency (%)
(49
87.5%
6
 
10.7%
[1
 
1.8%
Math Symbol
ValueCountFrequency (%)
~15
57.7%
+11
42.3%
Final Punctuation
ValueCountFrequency (%)
12
92.3%
1
 
7.7%
Initial Punctuation
ValueCountFrequency (%)
5
62.5%
3
37.5%
Space Separator
ValueCountFrequency (%)
23452
100.0%
Dash Punctuation
ValueCountFrequency (%)
-937
100.0%
Connector Punctuation
ValueCountFrequency (%)
_8
100.0%
Other Symbol
ValueCountFrequency (%)
1
100.0%
Control
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin131474
83.2%
Common26533
 
16.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
e15434
 
11.7%
s11399
 
8.7%
o9266
 
7.0%
r7925
 
6.0%
a7571
 
5.8%
i7425
 
5.6%
n6791
 
5.2%
S6605
 
5.0%
t6395
 
4.9%
l6277
 
4.8%
Other values (49)46386
35.3%
Common
ValueCountFrequency (%)
23452
88.4%
-937
 
3.5%
'430
 
1.6%
2322
 
1.2%
0312
 
1.2%
5239
 
0.9%
1162
 
0.6%
8105
 
0.4%
987
 
0.3%
369
 
0.3%
Other values (30)418
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII157966
> 99.9%
Punctuation21
 
< 0.1%
None19
 
< 0.1%
Letterlike Symbols1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
23452
14.8%
e15434
 
9.8%
s11399
 
7.2%
o9266
 
5.9%
r7925
 
5.0%
a7571
 
4.8%
i7425
 
4.7%
n6791
 
4.3%
S6605
 
4.2%
t6395
 
4.0%
Other values (75)55703
35.3%
Punctuation
ValueCountFrequency (%)
12
57.1%
5
23.8%
3
 
14.3%
1
 
4.8%
None
ValueCountFrequency (%)
6
31.6%
3
15.8%
ä3
15.8%
é2
 
10.5%
ß1
 
5.3%
ö1
 
5.3%
Ü1
 
5.3%
à1
 
5.3%
Ä1
 
5.3%
Letterlike Symbols
ValueCountFrequency (%)
1
100.0%

price
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct127
Distinct (%)8.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.356452242
Minimum1
Maximum49
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.1 KiB
2022-08-25T01:41:48.213333image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.66
Q15.825
median8
Q311
95-th percentile15
Maximum49
Range48
Interquartile range (IQR)5.175

Descriptive statistics

Standard deviation3.937160762
Coefficient of variation (CV)0.4711521886
Kurtosis7.863954254
Mean8.356452242
Median Absolute Deviation (MAD)3
Skewness1.327111243
Sum12860.58
Variance15.50123487
MonotonicityNot monotonic
2022-08-25T01:41:48.392143image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8277
18.0%
11197
12.8%
9125
 
8.1%
7124
 
8.1%
6116
 
7.5%
1281
 
5.3%
579
 
5.1%
1456
 
3.6%
1354
 
3.5%
1641
 
2.7%
Other values (117)389
25.3%
ValueCountFrequency (%)
15
0.3%
1.652
 
0.1%
1.662
 
0.1%
1.671
 
0.1%
1.684
0.3%
1.71
 
0.1%
1.711
 
0.1%
1.725
0.3%
1.741
 
0.1%
1.752
 
0.1%
ValueCountFrequency (%)
491
 
0.1%
271
 
0.1%
261
 
0.1%
251
 
0.1%
241
 
0.1%
231
 
0.1%
223
 
0.2%
204
0.3%
196
0.4%
189
0.6%

retail_price
Real number (ℝ≥0)

HIGH CORRELATION

Distinct104
Distinct (%)6.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.31708902
Minimum1
Maximum252
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.1 KiB
2022-08-25T01:41:48.549408image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q17
median10
Q326
95-th percentile85
Maximum252
Range251
Interquartile range (IQR)19

Descriptive statistics

Standard deviation30.30964239
Coefficient of variation (CV)1.299889637
Kurtosis10.05389115
Mean23.31708902
Median Absolute Deviation (MAD)5
Skewness2.737019061
Sum35885
Variance918.6744217
MonotonicityNot monotonic
2022-08-25T01:41:48.727422image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7172
 
11.2%
6132
 
8.6%
10125
 
8.1%
5100
 
6.5%
1198
 
6.4%
889
 
5.8%
953
 
3.4%
449
 
3.2%
1746
 
3.0%
242
 
2.7%
Other values (94)633
41.1%
ValueCountFrequency (%)
11
 
0.1%
242
 
2.7%
336
 
2.3%
449
 
3.2%
5100
6.5%
6132
8.6%
7172
11.2%
889
5.8%
953
 
3.4%
10125
8.1%
ValueCountFrequency (%)
2522
0.1%
2501
 
0.1%
1691
 
0.1%
1684
0.3%
1594
0.3%
1522
0.1%
1451
 
0.1%
1421
 
0.1%
1403
0.2%
1391
 
0.1%

units_sold
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct15
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4422.480182
Minimum1
Maximum100000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.1 KiB
2022-08-25T01:41:48.879432image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile50
Q1100
median1000
Q35000
95-th percentile20000
Maximum100000
Range99999
Interquartile range (IQR)4900

Descriptive statistics

Standard deviation9438.316389
Coefficient of variation (CV)2.134168159
Kurtosis44.74816421
Mean4422.480182
Median Absolute Deviation (MAD)900
Skewness5.576354077
Sum6806197
Variance89081816.26
MonotonicityNot monotonic
2022-08-25T01:41:49.014247image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
100493
32.0%
1000403
26.2%
5000216
14.0%
10000176
 
11.4%
20000103
 
6.7%
5068
 
4.4%
1044
 
2.9%
5000017
 
1.1%
1000006
 
0.4%
13
 
0.2%
Other values (5)10
 
0.6%
ValueCountFrequency (%)
13
 
0.2%
22
 
0.1%
32
 
0.1%
61
 
0.1%
72
 
0.1%
83
 
0.2%
1044
 
2.9%
5068
 
4.4%
100493
32.0%
1000403
26.2%
ValueCountFrequency (%)
1000006
 
0.4%
5000017
 
1.1%
20000103
 
6.7%
10000176
 
11.4%
5000216
14.0%
1000403
26.2%
100493
32.0%
5068
 
4.4%
1044
 
2.9%
83
 
0.2%

uses_ad_boosts
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size12.1 KiB
0
868 
1
671 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1539
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0868
56.4%
1671
43.6%

Length

2022-08-25T01:41:49.165267image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-08-25T01:41:49.299392image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0868
56.4%
1671
43.6%

Most occurring characters

ValueCountFrequency (%)
0868
56.4%
1671
43.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1539
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0868
56.4%
1671
43.6%

Most occurring scripts

ValueCountFrequency (%)
Common1539
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0868
56.4%
1671
43.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII1539
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0868
56.4%
1671
43.6%

rating
Real number (ℝ≥0)

HIGH CORRELATION

Distinct192
Distinct (%)12.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.822839506
Minimum1
Maximum5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.1 KiB
2022-08-25T01:41:50.841468image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q13.55
median3.85
Q34.11
95-th percentile4.67
Maximum5
Range4
Interquartile range (IQR)0.56

Descriptive statistics

Standard deviation0.50861901
Coefficient of variation (CV)0.1330474401
Kurtosis2.892141789
Mean3.822839506
Median Absolute Deviation (MAD)0.28
Skewness-0.5473695733
Sum5883.35
Variance0.2586932974
MonotonicityNot monotonic
2022-08-25T01:41:51.023483image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
565
 
4.2%
443
 
2.8%
3.6731
 
2.0%
4.0722
 
1.4%
3.821
 
1.4%
3.6120
 
1.3%
319
 
1.2%
3.7519
 
1.2%
4.0919
 
1.2%
4.1419
 
1.2%
Other values (182)1261
81.9%
ValueCountFrequency (%)
13
 
0.2%
1.52
 
0.1%
28
0.5%
2.251
 
0.1%
2.331
 
0.1%
2.442
 
0.1%
2.53
 
0.2%
2.572
 
0.1%
2.612
 
0.1%
2.675
0.3%
ValueCountFrequency (%)
565
4.2%
4.861
 
0.1%
4.831
 
0.1%
4.82
 
0.1%
4.754
 
0.3%
4.741
 
0.1%
4.676
 
0.4%
4.641
 
0.1%
4.632
 
0.1%
4.621
 
0.1%

rating_count
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct761
Distinct (%)49.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean907.3723197
Minimum0
Maximum20744
Zeros43
Zeros (%)2.8%
Negative0
Negative (%)0.0%
Memory size12.1 KiB
2022-08-25T01:41:51.216514image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q126
median161
Q3873
95-th percentile3822.5
Maximum20744
Range20744
Interquartile range (IQR)847

Descriptive statistics

Standard deviation2001.720315
Coefficient of variation (CV)2.206062794
Kurtosis29.38686326
Mean907.3723197
Median Absolute Deviation (MAD)157
Skewness4.74118781
Sum1396446
Variance4006884.218
MonotonicityNot monotonic
2022-08-25T01:41:51.373528image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
043
 
2.8%
228
 
1.8%
427
 
1.8%
626
 
1.7%
1224
 
1.6%
1019
 
1.2%
319
 
1.2%
117
 
1.1%
817
 
1.1%
1317
 
1.1%
Other values (751)1302
84.6%
ValueCountFrequency (%)
043
2.8%
117
 
1.1%
228
1.8%
319
1.2%
427
1.8%
511
 
0.7%
626
1.7%
712
 
0.8%
817
 
1.1%
98
 
0.5%
ValueCountFrequency (%)
207441
0.1%
184631
0.1%
183931
0.1%
179801
0.1%
174441
0.1%
145681
0.1%
137891
0.1%
134881
0.1%
131981
0.1%
128801
0.1%

rating_five_count
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct605
Distinct (%)39.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean438.1448993
Minimum0
Maximum11548
Zeros70
Zeros (%)4.5%
Negative0
Negative (%)0.0%
Memory size12.1 KiB
2022-08-25T01:41:51.532336image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q111
median77
Q3401
95-th percentile2045.5
Maximum11548
Range11548
Interquartile range (IQR)390

Descriptive statistics

Standard deviation977.5953676
Coefficient of variation (CV)2.231214763
Kurtosis34.33679413
Mean438.1448993
Median Absolute Deviation (MAD)75
Skewness4.946674927
Sum674305
Variance955692.7027
MonotonicityNot monotonic
2022-08-25T01:41:51.721126image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
070
 
4.5%
548
 
3.1%
146
 
3.0%
342
 
2.7%
240
 
2.6%
432
 
2.1%
925
 
1.6%
724
 
1.6%
821
 
1.4%
1720
 
1.3%
Other values (595)1171
76.1%
ValueCountFrequency (%)
070
4.5%
146
3.0%
240
2.6%
342
2.7%
432
2.1%
548
3.1%
619
 
1.2%
724
 
1.6%
821
 
1.4%
925
 
1.6%
ValueCountFrequency (%)
115481
0.1%
111841
0.1%
82901
0.1%
75301
0.1%
73371
0.1%
68621
0.1%
67691
0.1%
63251
0.1%
60601
0.1%
59461
0.1%

rating_four_count
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct440
Distinct (%)28.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean177.9220273
Minimum0
Maximum4152
Zeros130
Zeros (%)8.4%
Negative0
Negative (%)0.0%
Memory size12.1 KiB
2022-08-25T01:41:51.892116image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14.5
median30
Q3167
95-th percentile734.4
Maximum4152
Range4152
Interquartile range (IQR)162.5

Descriptive statistics

Standard deviation399.4437067
Coefficient of variation (CV)2.245049209
Kurtosis27.9005552
Mean177.9220273
Median Absolute Deviation (MAD)29
Skewness4.680767905
Sum273822
Variance159555.2748
MonotonicityNot monotonic
2022-08-25T01:41:52.052945image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0130
 
8.4%
187
 
5.7%
264
 
4.2%
452
 
3.4%
352
 
3.4%
549
 
3.2%
730
 
1.9%
1128
 
1.8%
826
 
1.7%
626
 
1.7%
Other values (430)995
64.7%
ValueCountFrequency (%)
0130
8.4%
187
5.7%
264
4.2%
352
 
3.4%
452
 
3.4%
549
 
3.2%
626
 
1.7%
730
 
1.9%
826
 
1.7%
917
 
1.1%
ValueCountFrequency (%)
41521
0.1%
34831
0.1%
34041
0.1%
33511
0.1%
31911
0.1%
30061
0.1%
29521
0.1%
28361
0.1%
27011
0.1%
26471
0.1%

rating_three_count
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct440
Distinct (%)28.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean177.9220273
Minimum0
Maximum4152
Zeros130
Zeros (%)8.4%
Negative0
Negative (%)0.0%
Memory size12.1 KiB
2022-08-25T01:41:52.211457image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14.5
median30
Q3167
95-th percentile734.4
Maximum4152
Range4152
Interquartile range (IQR)162.5

Descriptive statistics

Standard deviation399.4437067
Coefficient of variation (CV)2.245049209
Kurtosis27.9005552
Mean177.9220273
Median Absolute Deviation (MAD)29
Skewness4.680767905
Sum273822
Variance159555.2748
MonotonicityNot monotonic
2022-08-25T01:41:52.384469image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0130
 
8.4%
187
 
5.7%
264
 
4.2%
452
 
3.4%
352
 
3.4%
549
 
3.2%
730
 
1.9%
1128
 
1.8%
826
 
1.7%
626
 
1.7%
Other values (430)995
64.7%
ValueCountFrequency (%)
0130
8.4%
187
5.7%
264
4.2%
352
 
3.4%
452
 
3.4%
549
 
3.2%
626
 
1.7%
730
 
1.9%
826
 
1.7%
917
 
1.1%
ValueCountFrequency (%)
41521
0.1%
34831
0.1%
34041
0.1%
33511
0.1%
31911
0.1%
30061
0.1%
29521
0.1%
28361
0.1%
27011
0.1%
26471
0.1%

rating_two_count
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct262
Distinct (%)17.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean63.13385315
Minimum0
Maximum2003
Zeros228
Zeros (%)14.8%
Negative0
Negative (%)0.0%
Memory size12.1 KiB
2022-08-25T01:41:52.563483image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median11
Q361
95-th percentile271.7
Maximum2003
Range2003
Interquartile range (IQR)59

Descriptive statistics

Standard deviation150.9319562
Coefficient of variation (CV)2.390666001
Kurtosis45.08231125
Mean63.13385315
Median Absolute Deviation (MAD)11
Skewness5.68142204
Sum97163
Variance22780.45541
MonotonicityNot monotonic
2022-08-25T01:41:52.712952image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0228
 
14.8%
1151
 
9.8%
282
 
5.3%
367
 
4.4%
454
 
3.5%
540
 
2.6%
637
 
2.4%
735
 
2.3%
829
 
1.9%
923
 
1.5%
Other values (252)793
51.5%
ValueCountFrequency (%)
0228
14.8%
1151
9.8%
282
 
5.3%
367
 
4.4%
454
 
3.5%
540
 
2.6%
637
 
2.4%
735
 
2.3%
829
 
1.9%
923
 
1.5%
ValueCountFrequency (%)
20031
0.1%
17361
0.1%
14101
0.1%
13101
0.1%
11741
0.1%
11361
0.1%
10331
0.1%
9701
0.1%
9601
0.1%
9161
0.1%

rating_one_count
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct330
Distinct (%)21.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean94.85120208
Minimum0
Maximum2789
Zeros152
Zeros (%)9.9%
Negative0
Negative (%)0.0%
Memory size12.1 KiB
2022-08-25T01:41:52.878516image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median19
Q391.5
95-th percentile396.1
Maximum2789
Range2789
Interquartile range (IQR)88.5

Descriptive statistics

Standard deviation213.5208576
Coefficient of variation (CV)2.251113881
Kurtosis42.06094737
Mean94.85120208
Median Absolute Deviation (MAD)19
Skewness5.397757116
Sum145976
Variance45591.15665
MonotonicityNot monotonic
2022-08-25T01:41:53.017310image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0152
 
9.9%
1111
 
7.2%
270
 
4.5%
367
 
4.4%
458
 
3.8%
745
 
2.9%
538
 
2.5%
632
 
2.1%
831
 
2.0%
927
 
1.8%
Other values (320)908
59.0%
ValueCountFrequency (%)
0152
9.9%
1111
7.2%
270
4.5%
367
4.4%
458
 
3.8%
538
 
2.5%
632
 
2.1%
745
 
2.9%
831
 
2.0%
927
 
1.8%
ValueCountFrequency (%)
27891
0.1%
25591
0.1%
18461
0.1%
17361
0.1%
16001
0.1%
14041
0.1%
13291
0.1%
13152
0.1%
12711
0.1%
12431
0.1%

badges_count
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size12.1 KiB
0
1391 
1
 
135
2
 
11
3
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1539
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
01391
90.4%
1135
 
8.8%
211
 
0.7%
32
 
0.1%

Length

2022-08-25T01:41:53.166556image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-08-25T01:41:53.321521image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
01391
90.4%
1135
 
8.8%
211
 
0.7%
32
 
0.1%

Most occurring characters

ValueCountFrequency (%)
01391
90.4%
1135
 
8.8%
211
 
0.7%
32
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1539
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
01391
90.4%
1135
 
8.8%
211
 
0.7%
32
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common1539
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
01391
90.4%
1135
 
8.8%
211
 
0.7%
32
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII1539
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
01391
90.4%
1135
 
8.8%
211
 
0.7%
32
 
0.1%

badge_local_product
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size12.1 KiB
0
1510 
1
 
29

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1539
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
01510
98.1%
129
 
1.9%

Length

2022-08-25T01:41:53.440528image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-08-25T01:41:53.576141image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
01510
98.1%
129
 
1.9%

Most occurring characters

ValueCountFrequency (%)
01510
98.1%
129
 
1.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1539
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
01510
98.1%
129
 
1.9%

Most occurring scripts

ValueCountFrequency (%)
Common1539
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
01510
98.1%
129
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII1539
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
01510
98.1%
129
 
1.9%

badge_product_quality
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size12.1 KiB
0
1425 
1
 
114

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1539
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
01425
92.6%
1114
 
7.4%

Length

2022-08-25T01:41:53.701151image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-08-25T01:41:53.829255image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
01425
92.6%
1114
 
7.4%

Most occurring characters

ValueCountFrequency (%)
01425
92.6%
1114
 
7.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1539
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
01425
92.6%
1114
 
7.4%

Most occurring scripts

ValueCountFrequency (%)
Common1539
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
01425
92.6%
1114
 
7.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII1539
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
01425
92.6%
1114
 
7.4%

badge_fast_shipping
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size12.1 KiB
0
1519 
1
 
20

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1539
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
01519
98.7%
120
 
1.3%

Length

2022-08-25T01:41:53.935347image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-08-25T01:41:54.081039image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
01519
98.7%
120
 
1.3%

Most occurring characters

ValueCountFrequency (%)
01519
98.7%
120
 
1.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1539
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
01519
98.7%
120
 
1.3%

Most occurring scripts

ValueCountFrequency (%)
Common1539
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
01519
98.7%
120
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII1539
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
01519
98.7%
120
 
1.3%

tags
Categorical

HIGH CARDINALITY
UNIFORM

Distinct1230
Distinct (%)79.9%
Missing0
Missing (%)0.0%
Memory size12.1 KiB
Summer,Fashion,Necks,Skirts,Dress,Loose,Women's Fashion,Round neck,beach dress,sleeveless,Beach,Casual,Women
 
15
Summer,Sling,Dresses,Dress,V-neck,Casual,Pocket,Women's Fashion,Sleeveless dress,women dress,Floral,sleeveless,Women,loose dress,Pleated,casual dress
 
9
slimming,wasitcincher,Fashion,waistgirdle,slimmingcorset,Corset,Summer,Waist,waist trainer,Fashion Accessory,Vest,shaperwear,belt
 
8
Summer,Fashion,Necks,Beach,Dress,Loose,beach dress,Round neck,Women's Fashion,sleeveless,Skirts,Casual,Women
 
7
Summer,Women Rompers,Plus Size,women long pants,linenjumpsuit,pants,Overalls,Loose,plussizejumpsuit,Women's Fashion,strappant,Long pants,Jumpsuits & Rompers,rompers womens jumpsuit,Vintage,Women,women Jumpsuit,Casual,jumpsuit
 
6
Other values (1225)
1494 

Length

Max length448
Median length241
Mean length169.3417804
Min length61

Characters and Unicode

Total characters260617
Distinct characters85
Distinct categories8 ?
Distinct scripts5 ?
Distinct blocks5 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1014 ?
Unique (%)65.9%

Sample

1st rowSummer,Fashion,womenunderwearsuit,printedpajamasset,womencasualshort,Women's Fashion,flamingo,loungewearset,Casual,Shirt,casualsleepwear,Shorts,flamingotshirt,Elastic,Vintage,Tops,tshirtandshortsset,Women,Sleepwear,Print,womenpajamasset,womennightwear,Pajamas,womensleepwearset
2nd rowMini,womens dresses,Summer,Patchwork,fashion dress,Dress,Mini dress,Women's Fashion,Women S Clothing,backless,party,summer dresses,sleeveless,sexy,Casual
3rd rowSummer,cardigan,women beachwear,chiffon,Sexy women,Coat,summercardigan,openfront,short sleeves,Swimsuit,Women's Fashion,leaf,Green,printed,Spring,longcardigan,Women,Beach,kimono
4th rowSummer,Shorts,Cotton,Cotton T Shirt,Sleeve,printedletterstop,Clothing,Tops,Necks,short sleeves,Women's Fashion,Women Clothing,printed,Women,tshirtforwomen,Fashion,T Shirts,Shirt
5th rowSummer,Plus Size,Lace,Casual pants,Bottom,pants,Loose,Women's Fashion,Shorts,Lace Up,Elastic,Casual,Women

Common Values

ValueCountFrequency (%)
Summer,Fashion,Necks,Skirts,Dress,Loose,Women's Fashion,Round neck,beach dress,sleeveless,Beach,Casual,Women15
 
1.0%
Summer,Sling,Dresses,Dress,V-neck,Casual,Pocket,Women's Fashion,Sleeveless dress,women dress,Floral,sleeveless,Women,loose dress,Pleated,casual dress9
 
0.6%
slimming,wasitcincher,Fashion,waistgirdle,slimmingcorset,Corset,Summer,Waist,waist trainer,Fashion Accessory,Vest,shaperwear,belt8
 
0.5%
Summer,Fashion,Necks,Beach,Dress,Loose,beach dress,Round neck,Women's Fashion,sleeveless,Skirts,Casual,Women7
 
0.5%
Summer,Women Rompers,Plus Size,women long pants,linenjumpsuit,pants,Overalls,Loose,plussizejumpsuit,Women's Fashion,strappant,Long pants,Jumpsuits & Rompers,rompers womens jumpsuit,Vintage,Women,women Jumpsuit,Casual,jumpsuit6
 
0.4%
Summer,Leggings,Fashion,high waist,pants,slim,Women's Fashion,trousers,Green,Army,Women6
 
0.4%
Summer,short sleeve dress,neck dress,Necks,Sleeve,Beach,Dress,Loose,short sleeves,V-neck,Shorts,beach dress,Plus Size,Midi Dress,summer dress,Print,Pullovers,Women's Fashion,Casual,Women6
 
0.4%
pajamaset,Fashion,sexy pajamas for womens,silksleepwearforwomen,silksleepwear,pajamassuit,Casual,Women's Fashion,Summer,Sleepwear,pajamasforwomen,silksleepwearnightgown,silk,pajamassleepwear,women's pajamas,Women5
 
0.3%
Summer,Shorts,high waist shorts,high waist,Casual pants,pants,summer shorts,Waist,Slim Fit,Short pants,Women's Fashion,Plus Size,Lace Up,Women,Fashion,Casual,Lace5
 
0.3%
Mini,womens dresses,Summer,sleevele,Dress,Mini dress,Women's Fashion,Fashion,backless,party,sexy,summer dresses,Women S Clothing,Casual,sleeveless5
 
0.3%
Other values (1220)1467
95.3%

Length

2022-08-25T01:41:54.228050image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
summer,plus196
 
1.6%
163
 
1.4%
dress,women's159
 
1.3%
dress138
 
1.2%
t106
 
0.9%
sleeve100
 
0.8%
for93
 
0.8%
fashion,plus81
 
0.7%
tank81
 
0.7%
fashion,sleeveless76
 
0.6%
Other values (5417)10792
90.0%

Most occurring characters

ValueCountFrequency (%)
s28513
 
10.9%
e25958
 
10.0%
,25251
 
9.7%
o16404
 
6.3%
r13699
 
5.3%
n13594
 
5.2%
i13353
 
5.1%
a12965
 
5.0%
t12578
 
4.8%
10453
 
4.0%
Other values (75)87849
33.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter200773
77.0%
Other Punctuation26871
 
10.3%
Uppercase Letter21810
 
8.4%
Space Separator10453
 
4.0%
Dash Punctuation621
 
0.2%
Decimal Number80
 
< 0.1%
Other Letter5
 
< 0.1%
Connector Punctuation4
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
s28513
14.2%
e25958
12.9%
o16404
 
8.2%
r13699
 
6.8%
n13594
 
6.8%
i13353
 
6.7%
a12965
 
6.5%
t12578
 
6.3%
m10037
 
5.0%
l9390
 
4.7%
Other values (29)44282
22.1%
Uppercase Letter
ValueCountFrequency (%)
S5431
24.9%
F3277
15.0%
W2808
12.9%
C1700
 
7.8%
T1534
 
7.0%
P1457
 
6.7%
D1228
 
5.6%
L869
 
4.0%
B774
 
3.5%
V629
 
2.9%
Other values (14)2103
 
9.6%
Decimal Number
ValueCountFrequency (%)
225
31.2%
319
23.8%
49
 
11.2%
09
 
11.2%
18
 
10.0%
53
 
3.8%
93
 
3.8%
82
 
2.5%
72
 
2.5%
Other Punctuation
ValueCountFrequency (%)
,25251
94.0%
'1432
 
5.3%
&165
 
0.6%
#21
 
0.1%
/2
 
< 0.1%
Other Letter
ValueCountFrequency (%)
1
20.0%
1
20.0%
1
20.0%
1
20.0%
1
20.0%
Space Separator
ValueCountFrequency (%)
10453
100.0%
Dash Punctuation
ValueCountFrequency (%)
-621
100.0%
Connector Punctuation
ValueCountFrequency (%)
_4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin222571
85.4%
Common38029
 
14.6%
Cyrillic12
 
< 0.1%
Han4
 
< 0.1%
Hiragana1
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
s28513
12.8%
e25958
 
11.7%
o16404
 
7.4%
r13699
 
6.2%
n13594
 
6.1%
i13353
 
6.0%
a12965
 
5.8%
t12578
 
5.7%
m10037
 
4.5%
l9390
 
4.2%
Other values (41)66080
29.7%
Common
ValueCountFrequency (%)
,25251
66.4%
10453
27.5%
'1432
 
3.8%
-621
 
1.6%
&165
 
0.4%
225
 
0.1%
#21
 
0.1%
319
 
< 0.1%
49
 
< 0.1%
09
 
< 0.1%
Other values (7)24
 
0.1%
Cyrillic
ValueCountFrequency (%)
м1
8.3%
ж1
8.3%
у1
8.3%
и1
8.3%
с1
8.3%
к1
8.3%
т1
8.3%
ы1
8.3%
р1
8.3%
о1
8.3%
Other values (2)2
16.7%
Han
ValueCountFrequency (%)
1
25.0%
1
25.0%
1
25.0%
1
25.0%
Hiragana
ValueCountFrequency (%)
1
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII260599
> 99.9%
Cyrillic12
 
< 0.1%
CJK4
 
< 0.1%
Hiragana1
 
< 0.1%
None1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
s28513
 
10.9%
e25958
 
10.0%
,25251
 
9.7%
o16404
 
6.3%
r13699
 
5.3%
n13594
 
5.2%
i13353
 
5.1%
a12965
 
5.0%
t12578
 
4.8%
10453
 
4.0%
Other values (57)87831
33.7%
Cyrillic
ValueCountFrequency (%)
м1
8.3%
ж1
8.3%
у1
8.3%
и1
8.3%
с1
8.3%
к1
8.3%
т1
8.3%
ы1
8.3%
р1
8.3%
о1
8.3%
Other values (2)2
16.7%
CJK
ValueCountFrequency (%)
1
25.0%
1
25.0%
1
25.0%
1
25.0%
Hiragana
ValueCountFrequency (%)
1
100.0%
None
ValueCountFrequency (%)
é1
100.0%

product_color
Categorical

HIGH CARDINALITY

Distinct102
Distinct (%)6.6%
Missing0
Missing (%)0.0%
Memory size12.1 KiB
black
298 
white
246 
yellow
101 
pink
97 
blue
96 
Other values (97)
701 

Length

Max length19
Median length18
Mean length5.601039636
Min length3

Characters and Unicode

Total characters8620
Distinct characters34
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique40 ?
Unique (%)2.6%

Sample

1st rowwhite
2nd rowgreen
3rd rowleopardprint
4th rowblack
5th rowyellow

Common Values

ValueCountFrequency (%)
black298
19.4%
white246
16.0%
yellow101
 
6.6%
pink97
 
6.3%
blue96
 
6.2%
red92
 
6.0%
green86
 
5.6%
grey70
 
4.5%
purple53
 
3.4%
no_color41
 
2.7%
Other values (92)359
23.3%

Length

2022-08-25T01:41:54.398143image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
black317
19.5%
white269
16.5%
pink108
 
6.6%
yellow105
 
6.5%
blue105
 
6.5%
green104
 
6.4%
red97
 
6.0%
grey72
 
4.4%
purple53
 
3.3%
no_color41
 
2.5%
Other values (65)356
21.9%

Most occurring characters

ValueCountFrequency (%)
e1256
14.6%
l904
 
10.5%
r616
 
7.1%
i510
 
5.9%
b505
 
5.9%
a488
 
5.7%
k480
 
5.6%
n422
 
4.9%
w420
 
4.9%
c403
 
4.7%
Other values (24)2616
30.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter8433
97.8%
Space Separator88
 
1.0%
Connector Punctuation41
 
0.5%
Other Punctuation40
 
0.5%
Uppercase Letter15
 
0.2%
Dash Punctuation3
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e1256
14.9%
l904
 
10.7%
r616
 
7.3%
i510
 
6.0%
b505
 
6.0%
a488
 
5.8%
k480
 
5.7%
n422
 
5.0%
w420
 
5.0%
c403
 
4.8%
Other values (13)2429
28.8%
Uppercase Letter
ValueCountFrequency (%)
B4
26.7%
W3
20.0%
P2
13.3%
A2
13.3%
R2
13.3%
E1
 
6.7%
D1
 
6.7%
Space Separator
ValueCountFrequency (%)
88
100.0%
Connector Punctuation
ValueCountFrequency (%)
_41
100.0%
Other Punctuation
ValueCountFrequency (%)
&40
100.0%
Dash Punctuation
ValueCountFrequency (%)
-3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin8448
98.0%
Common172
 
2.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e1256
14.9%
l904
 
10.7%
r616
 
7.3%
i510
 
6.0%
b505
 
6.0%
a488
 
5.8%
k480
 
5.7%
n422
 
5.0%
w420
 
5.0%
c403
 
4.8%
Other values (20)2444
28.9%
Common
ValueCountFrequency (%)
88
51.2%
_41
23.8%
&40
23.3%
-3
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII8620
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e1256
14.6%
l904
 
10.5%
r616
 
7.1%
i510
 
5.9%
b505
 
5.9%
a488
 
5.7%
k480
 
5.6%
n422
 
4.9%
w420
 
4.9%
c403
 
4.7%
Other values (24)2616
30.3%

product_variation_size_id
Categorical

HIGH CARDINALITY

Distinct107
Distinct (%)7.0%
Missing0
Missing (%)0.0%
Memory size12.1 KiB
S
630 
XS
344 
M
198 
XXS
94 
L
 
49
Other values (102)
224 

Length

Max length28
Median length1
Mean length1.983755686
Min length1

Characters and Unicode

Total characters3053
Distinct characters63
Distinct categories9 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique64 ?
Unique (%)4.2%

Sample

1st rowM
2nd rowXS
3rd rowXS
4th rowM
5th rowS

Common Values

ValueCountFrequency (%)
S630
40.9%
XS344
22.4%
M198
 
12.9%
XXS94
 
6.1%
L49
 
3.2%
XL17
 
1.1%
S.16
 
1.0%
XXL15
 
1.0%
no_size14
 
0.9%
XXXS6
 
0.4%
Other values (97)156
 
10.1%

Length

2022-08-25T01:41:54.544967image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
s666
41.0%
xs353
21.7%
m204
 
12.5%
xxs98
 
6.0%
l51
 
3.1%
size25
 
1.5%
xl18
 
1.1%
xxl15
 
0.9%
no_size14
 
0.9%
16
 
0.4%
Other values (111)176
 
10.8%

Most occurring characters

ValueCountFrequency (%)
S1190
39.0%
X667
21.8%
M208
 
6.8%
L114
 
3.7%
90
 
2.9%
i68
 
2.2%
e68
 
2.2%
z50
 
1.6%
s38
 
1.2%
.36
 
1.2%
Other values (53)524
17.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter2263
74.1%
Lowercase Letter446
 
14.6%
Decimal Number152
 
5.0%
Space Separator90
 
2.9%
Other Punctuation42
 
1.4%
Dash Punctuation28
 
0.9%
Connector Punctuation14
 
0.5%
Open Punctuation9
 
0.3%
Close Punctuation9
 
0.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i68
15.2%
e68
15.2%
z50
11.2%
s38
8.5%
o30
 
6.7%
n29
 
6.5%
c24
 
5.4%
a23
 
5.2%
t21
 
4.7%
m17
 
3.8%
Other values (14)78
17.5%
Uppercase Letter
ValueCountFrequency (%)
S1190
52.6%
X667
29.5%
M208
 
9.2%
L114
 
5.0%
E14
 
0.6%
I11
 
0.5%
U10
 
0.4%
Z9
 
0.4%
P8
 
0.4%
C6
 
0.3%
Other values (11)26
 
1.1%
Decimal Number
ValueCountFrequency (%)
331
20.4%
024
15.8%
221
13.8%
119
12.5%
517
11.2%
416
10.5%
88
 
5.3%
67
 
4.6%
95
 
3.3%
74
 
2.6%
Other Punctuation
ValueCountFrequency (%)
.36
85.7%
&3
 
7.1%
/3
 
7.1%
Space Separator
ValueCountFrequency (%)
90
100.0%
Dash Punctuation
ValueCountFrequency (%)
-28
100.0%
Connector Punctuation
ValueCountFrequency (%)
_14
100.0%
Open Punctuation
ValueCountFrequency (%)
(9
100.0%
Close Punctuation
ValueCountFrequency (%)
)9
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin2709
88.7%
Common344
 
11.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
S1190
43.9%
X667
24.6%
M208
 
7.7%
L114
 
4.2%
i68
 
2.5%
e68
 
2.5%
z50
 
1.8%
s38
 
1.4%
o30
 
1.1%
n29
 
1.1%
Other values (35)247
 
9.1%
Common
ValueCountFrequency (%)
90
26.2%
.36
 
10.5%
331
 
9.0%
-28
 
8.1%
024
 
7.0%
221
 
6.1%
119
 
5.5%
517
 
4.9%
416
 
4.7%
_14
 
4.1%
Other values (8)48
14.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII3053
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
S1190
39.0%
X667
21.8%
M208
 
6.8%
L114
 
3.7%
90
 
2.9%
i68
 
2.2%
e68
 
2.2%
z50
 
1.6%
s38
 
1.2%
.36
 
1.2%
Other values (53)524
17.2%

product_variation_inventory
Real number (ℝ≥0)

Distinct48
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33.34762833
Minimum1
Maximum50
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.1 KiB
2022-08-25T01:41:54.697314image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q17
median50
Q350
95-th percentile50
Maximum50
Range49
Interquartile range (IQR)43

Descriptive statistics

Standard deviation21.24629577
Coefficient of variation (CV)0.637115646
Kurtosis-1.543589937
Mean33.34762833
Median Absolute Deviation (MAD)0
Skewness-0.5901065409
Sum51322
Variance451.4050838
MonotonicityNot monotonic
2022-08-25T01:41:54.860324image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
50895
58.2%
1145
 
9.4%
277
 
5.0%
569
 
4.5%
349
 
3.2%
1038
 
2.5%
425
 
1.6%
922
 
1.4%
718
 
1.2%
618
 
1.2%
Other values (38)183
 
11.9%
ValueCountFrequency (%)
1145
9.4%
277
5.0%
349
 
3.2%
425
 
1.6%
569
4.5%
618
 
1.2%
718
 
1.2%
86
 
0.4%
922
 
1.4%
1038
 
2.5%
ValueCountFrequency (%)
50895
58.2%
499
 
0.6%
484
 
0.3%
474
 
0.3%
465
 
0.3%
453
 
0.2%
446
 
0.4%
434
 
0.3%
414
 
0.3%
402
 
0.1%

shipping_option_price
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct8
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.356725146
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.1 KiB
2022-08-25T01:41:55.021336image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median2
Q33
95-th percentile4
Maximum12
Range11
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.026678554
Coefficient of variation (CV)0.4356377981
Kurtosis6.567907465
Mean2.356725146
Median Absolute Deviation (MAD)1
Skewness1.366955377
Sum3627
Variance1.054068852
MonotonicityNot monotonic
2022-08-25T01:41:55.127539image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
2602
39.1%
3516
33.5%
1296
19.2%
475
 
4.9%
532
 
2.1%
612
 
0.8%
75
 
0.3%
121
 
0.1%
ValueCountFrequency (%)
1296
19.2%
2602
39.1%
3516
33.5%
475
 
4.9%
532
 
2.1%
612
 
0.8%
75
 
0.3%
121
 
0.1%
ValueCountFrequency (%)
121
 
0.1%
75
 
0.3%
612
 
0.8%
532
 
2.1%
475
 
4.9%
3516
33.5%
2602
39.1%
1296
19.2%

shipping_is_express
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size12.1 KiB
0
1535 
1
 
4

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1539
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
01535
99.7%
14
 
0.3%

Length

2022-08-25T01:41:55.261551image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-08-25T01:41:55.391561image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
01535
99.7%
14
 
0.3%

Most occurring characters

ValueCountFrequency (%)
01535
99.7%
14
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1539
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
01535
99.7%
14
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
Common1539
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
01535
99.7%
14
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII1539
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
01535
99.7%
14
 
0.3%

countries_shipped_to
Real number (ℝ≥0)

Distinct94
Distinct (%)6.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40.45094217
Minimum6
Maximum140
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.1 KiB
2022-08-25T01:41:55.521631image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile18
Q131
median40
Q343
95-th percentile71.1
Maximum140
Range134
Interquartile range (IQR)12

Descriptive statistics

Standard deviation20.13770936
Coefficient of variation (CV)0.4978304157
Kurtosis11.55946634
Mean40.45094217
Median Absolute Deviation (MAD)5
Skewness2.972676949
Sum62254
Variance405.5273382
MonotonicityNot monotonic
2022-08-25T01:41:55.690997image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
41169
 
11.0%
43167
 
10.9%
40104
 
6.8%
3874
 
4.8%
3663
 
4.1%
3559
 
3.8%
4256
 
3.6%
3942
 
2.7%
3737
 
2.4%
2537
 
2.4%
Other values (84)731
47.5%
ValueCountFrequency (%)
61
 
0.1%
85
 
0.3%
94
 
0.3%
107
0.5%
112
 
0.1%
122
 
0.1%
134
 
0.3%
1416
1.0%
154
 
0.3%
163
 
0.2%
ValueCountFrequency (%)
1403
 
0.2%
13913
0.8%
1389
0.6%
1373
 
0.2%
1352
 
0.1%
1322
 
0.1%
1274
 
0.3%
1251
 
0.1%
1241
 
0.1%
1183
 
0.2%

inventory_total
Real number (ℝ≥0)

Distinct10
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49.81741391
Minimum1
Maximum50
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.1 KiB
2022-08-25T01:41:55.866012image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile50
Q150
median50
Q350
95-th percentile50
Maximum50
Range49
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.590832757
Coefficient of variation (CV)0.05200656866
Kurtosis280.9706278
Mean49.81741391
Median Absolute Deviation (MAD)0
Skewness-16.29722299
Sum76669
Variance6.712414374
MonotonicityNot monotonic
2022-08-25T01:41:56.014026image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
501529
99.4%
22
 
0.1%
401
 
0.1%
361
 
0.1%
11
 
0.1%
301
 
0.1%
91
 
0.1%
241
 
0.1%
371
 
0.1%
381
 
0.1%
ValueCountFrequency (%)
11
 
0.1%
22
 
0.1%
91
 
0.1%
241
 
0.1%
301
 
0.1%
361
 
0.1%
371
 
0.1%
381
 
0.1%
401
 
0.1%
501529
99.4%
ValueCountFrequency (%)
501529
99.4%
401
 
0.1%
381
 
0.1%
371
 
0.1%
361
 
0.1%
301
 
0.1%
241
 
0.1%
91
 
0.1%
22
 
0.1%
11
 
0.1%
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size12.1 KiB
0
1071 
1
468 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1539
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
01071
69.6%
1468
30.4%

Length

2022-08-25T01:41:56.169039image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-08-25T01:41:56.367050image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
01071
69.6%
1468
30.4%

Most occurring characters

ValueCountFrequency (%)
01071
69.6%
1468
30.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1539
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
01071
69.6%
1468
30.4%

Most occurring scripts

ValueCountFrequency (%)
Common1539
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
01071
69.6%
1468
30.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII1539
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
01071
69.6%
1468
30.4%

origin_country
Categorical

HIGH CORRELATION

Distinct7
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size12.1 KiB
CN
1484 
US
 
31
no_origin_country
 
16
VE
 
4
SG
 
2
Other values (2)
 
2

Length

Max length17
Median length2
Mean length2.155945419
Min length2

Characters and Unicode

Total characters3318
Distinct characters20
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)0.1%

Sample

1st rowCN
2nd rowCN
3rd rowCN
4th rowCN
5th rowCN

Common Values

ValueCountFrequency (%)
CN1484
96.4%
US31
 
2.0%
no_origin_country16
 
1.0%
VE4
 
0.3%
SG2
 
0.1%
AT1
 
0.1%
GB1
 
0.1%

Length

2022-08-25T01:41:56.573066image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-08-25T01:41:56.829087image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
cn1484
96.4%
us31
 
2.0%
no_origin_country16
 
1.0%
ve4
 
0.3%
sg2
 
0.1%
at1
 
0.1%
gb1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
C1484
44.7%
N1484
44.7%
n48
 
1.4%
o48
 
1.4%
S33
 
1.0%
_32
 
1.0%
r32
 
1.0%
i32
 
1.0%
U31
 
0.9%
t16
 
0.5%
Other values (10)78
 
2.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter3046
91.8%
Lowercase Letter240
 
7.2%
Connector Punctuation32
 
1.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
C1484
48.7%
N1484
48.7%
S33
 
1.1%
U31
 
1.0%
V4
 
0.1%
E4
 
0.1%
G3
 
0.1%
A1
 
< 0.1%
T1
 
< 0.1%
B1
 
< 0.1%
Lowercase Letter
ValueCountFrequency (%)
n48
20.0%
o48
20.0%
r32
13.3%
i32
13.3%
t16
 
6.7%
y16
 
6.7%
c16
 
6.7%
u16
 
6.7%
g16
 
6.7%
Connector Punctuation
ValueCountFrequency (%)
_32
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin3286
99.0%
Common32
 
1.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
C1484
45.2%
N1484
45.2%
n48
 
1.5%
o48
 
1.5%
S33
 
1.0%
r32
 
1.0%
i32
 
1.0%
U31
 
0.9%
t16
 
0.5%
y16
 
0.5%
Other values (9)62
 
1.9%
Common
ValueCountFrequency (%)
_32
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII3318
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
C1484
44.7%
N1484
44.7%
n48
 
1.4%
o48
 
1.4%
S33
 
1.0%
_32
 
1.0%
r32
 
1.0%
i32
 
1.0%
U31
 
0.9%
t16
 
0.5%
Other values (10)78
 
2.4%

merchant_title
Categorical

HIGH CARDINALITY

Distinct958
Distinct (%)62.2%
Missing0
Missing (%)0.0%
Memory size12.1 KiB
guangzhouweishiweifushiyouxiangongsi
 
14
Suyi Technology
 
12
Sangboo Store
 
8
Cenic Beauty
 
8
shuilingjiao international trade company
 
8
Other values (953)
1489 

Length

Max length51
Median length38
Mean length12.2560104
Min length2

Characters and Unicode

Total characters18862
Distinct characters75
Distinct categories11 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique642 ?
Unique (%)41.7%

Sample

1st rowzgrdejia
2nd rowSaraHouse
3rd rowhxt520
4th rowallenfan
5th rowyoungpeopleshop

Common Values

ValueCountFrequency (%)
guangzhouweishiweifushiyouxiangongsi14
 
0.9%
Suyi Technology12
 
0.8%
Sangboo Store8
 
0.5%
Cenic Beauty8
 
0.5%
shuilingjiao international trade company8
 
0.5%
sjhdstoer8
 
0.5%
Pentiumhorse7
 
0.5%
witkey BL6
 
0.4%
zuilangmanDS6
 
0.4%
fengjinying6
 
0.4%
Other values (948)1456
94.6%

Length

2022-08-25T01:41:57.041084image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
fashion56
 
2.6%
store40
 
1.8%
international22
 
1.0%
technology17
 
0.8%
boutique17
 
0.8%
shop16
 
0.7%
the15
 
0.7%
guangzhouweishiweifushiyouxiangongsi14
 
0.6%
ltd14
 
0.6%
co14
 
0.6%
Other values (1145)1958
89.7%

Most occurring characters

ValueCountFrequency (%)
n1492
 
7.9%
i1485
 
7.9%
a1423
 
7.5%
o1281
 
6.8%
e1262
 
6.7%
u851
 
4.5%
s813
 
4.3%
h805
 
4.3%
g777
 
4.1%
l693
 
3.7%
Other values (65)7980
42.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter15277
81.0%
Uppercase Letter1950
 
10.3%
Decimal Number801
 
4.2%
Space Separator659
 
3.5%
Other Punctuation123
 
0.7%
Connector Punctuation37
 
0.2%
Dash Punctuation10
 
0.1%
Math Symbol2
 
< 0.1%
Open Punctuation1
 
< 0.1%
Close Punctuation1
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n1492
 
9.8%
i1485
 
9.7%
a1423
 
9.3%
o1281
 
8.4%
e1262
 
8.3%
u851
 
5.6%
s813
 
5.3%
h805
 
5.3%
g777
 
5.1%
l693
 
4.5%
Other values (16)4395
28.8%
Uppercase Letter
ValueCountFrequency (%)
S247
 
12.7%
L146
 
7.5%
A121
 
6.2%
H119
 
6.1%
T117
 
6.0%
O104
 
5.3%
F95
 
4.9%
C89
 
4.6%
M87
 
4.5%
N85
 
4.4%
Other values (16)740
37.9%
Decimal Number
ValueCountFrequency (%)
1137
17.1%
6125
15.6%
0106
13.2%
895
11.9%
288
11.0%
961
7.6%
559
7.4%
347
 
5.9%
445
 
5.6%
738
 
4.7%
Other Punctuation
ValueCountFrequency (%)
.73
59.3%
'19
 
15.4%
,17
 
13.8%
@7
 
5.7%
&4
 
3.3%
!3
 
2.4%
Space Separator
ValueCountFrequency (%)
659
100.0%
Connector Punctuation
ValueCountFrequency (%)
_37
100.0%
Dash Punctuation
ValueCountFrequency (%)
-10
100.0%
Math Symbol
ValueCountFrequency (%)
~2
100.0%
Open Punctuation
ValueCountFrequency (%)
(1
100.0%
Close Punctuation
ValueCountFrequency (%)
)1
100.0%
Initial Punctuation
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin17227
91.3%
Common1635
 
8.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
n1492
 
8.7%
i1485
 
8.6%
a1423
 
8.3%
o1281
 
7.4%
e1262
 
7.3%
u851
 
4.9%
s813
 
4.7%
h805
 
4.7%
g777
 
4.5%
l693
 
4.0%
Other values (42)6345
36.8%
Common
ValueCountFrequency (%)
659
40.3%
1137
 
8.4%
6125
 
7.6%
0106
 
6.5%
895
 
5.8%
288
 
5.4%
.73
 
4.5%
961
 
3.7%
559
 
3.6%
347
 
2.9%
Other values (13)185
 
11.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII18861
> 99.9%
Punctuation1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n1492
 
7.9%
i1485
 
7.9%
a1423
 
7.5%
o1281
 
6.8%
e1262
 
6.7%
u851
 
4.5%
s813
 
4.3%
h805
 
4.3%
g777
 
4.1%
l693
 
3.7%
Other values (64)7979
42.3%
Punctuation
ValueCountFrequency (%)
1
100.0%

merchant_name
Categorical

HIGH CARDINALITY

Distinct958
Distinct (%)62.2%
Missing0
Missing (%)0.0%
Memory size12.1 KiB
广州唯适唯服饰有限公司
 
14
greatexpectationstechnology
 
12
sangboostore
 
8
cenicbeauty
 
8
shuilingjiaointernationaltradecompany
 
8
Other values (953)
1489 

Length

Max length52
Median length40
Mean length11.75958415
Min length2

Characters and Unicode

Total characters18098
Distinct characters151
Distinct categories4 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique642 ?
Unique (%)41.7%

Sample

1st rowzgrdejia
2nd rowsarahouse
3rd rowhxt520
4th rowallenfan
5th rowhappyhorses

Common Values

ValueCountFrequency (%)
广州唯适唯服饰有限公司14
 
0.9%
greatexpectationstechnology12
 
0.8%
sangboostore8
 
0.5%
cenicbeauty8
 
0.5%
shuilingjiaointernationaltradecompany8
 
0.5%
sjhdstoer8
 
0.5%
pentiumhorse7
 
0.5%
witkeybl6
 
0.4%
zuilangmands6
 
0.4%
fengjinying6
 
0.4%
Other values (948)1456
94.6%

Length

2022-08-25T01:41:57.231567image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
广州唯适唯服饰有限公司14
 
0.9%
greatexpectationstechnology12
 
0.8%
sangboostore8
 
0.5%
cenicbeauty8
 
0.5%
shuilingjiaointernationaltradecompany8
 
0.5%
sjhdstoer8
 
0.5%
pentiumhorse7
 
0.5%
sklioppp6
 
0.4%
snowgirl6
 
0.4%
smarthomeinternationalcoltd6
 
0.4%
Other values (948)1456
94.6%

Most occurring characters

ValueCountFrequency (%)
a1536
 
8.5%
n1474
 
8.1%
i1418
 
7.8%
e1350
 
7.5%
o1299
 
7.2%
s959
 
5.3%
l844
 
4.7%
h816
 
4.5%
t790
 
4.4%
g761
 
4.2%
Other values (141)6851
37.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter16466
91.0%
Decimal Number1084
 
6.0%
Other Letter510
 
2.8%
Connector Punctuation38
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
35
 
6.9%
32
 
6.3%
32
 
6.3%
32
 
6.3%
28
 
5.5%
24
 
4.7%
21
 
4.1%
广17
 
3.3%
16
 
3.1%
14
 
2.7%
Other values (104)259
50.8%
Lowercase Letter
ValueCountFrequency (%)
a1536
 
9.3%
n1474
 
9.0%
i1418
 
8.6%
e1350
 
8.2%
o1299
 
7.9%
s959
 
5.8%
l844
 
5.1%
h816
 
5.0%
t790
 
4.8%
g761
 
4.6%
Other values (16)5219
31.7%
Decimal Number
ValueCountFrequency (%)
1190
17.5%
6154
14.2%
0148
13.7%
8126
11.6%
2116
10.7%
584
7.7%
979
7.3%
366
 
6.1%
465
 
6.0%
756
 
5.2%
Connector Punctuation
ValueCountFrequency (%)
_38
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin16466
91.0%
Common1122
 
6.2%
Han510
 
2.8%

Most frequent character per script

Han
ValueCountFrequency (%)
35
 
6.9%
32
 
6.3%
32
 
6.3%
32
 
6.3%
28
 
5.5%
24
 
4.7%
21
 
4.1%
广17
 
3.3%
16
 
3.1%
14
 
2.7%
Other values (104)259
50.8%
Latin
ValueCountFrequency (%)
a1536
 
9.3%
n1474
 
9.0%
i1418
 
8.6%
e1350
 
8.2%
o1299
 
7.9%
s959
 
5.8%
l844
 
5.1%
h816
 
5.0%
t790
 
4.8%
g761
 
4.6%
Other values (16)5219
31.7%
Common
ValueCountFrequency (%)
1190
16.9%
6154
13.7%
0148
13.2%
8126
11.2%
2116
10.3%
584
7.5%
979
7.0%
366
 
5.9%
465
 
5.8%
756
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII17588
97.2%
CJK510
 
2.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a1536
 
8.7%
n1474
 
8.4%
i1418
 
8.1%
e1350
 
7.7%
o1299
 
7.4%
s959
 
5.5%
l844
 
4.8%
h816
 
4.6%
t790
 
4.5%
g761
 
4.3%
Other values (27)6341
36.1%
CJK
ValueCountFrequency (%)
35
 
6.9%
32
 
6.3%
32
 
6.3%
32
 
6.3%
28
 
5.5%
24
 
4.7%
21
 
4.1%
广17
 
3.3%
16
 
3.1%
14
 
2.7%
Other values (104)259
50.8%

merchant_info_subtitle
Categorical

HIGH CARDINALITY
UNIFORM

Distinct1059
Distinct (%)68.8%
Missing0
Missing (%)0.0%
Memory size12.1 KiB
83 % avis positifs (32,168 notes)
 
13
86 % avis positifs (12,309 notes)
 
11
87 % avis positifs (42,919 notes)
 
8
86 % avis positifs (65,189 notes)
 
6
89 % avis positifs (55,499 notes)
 
6
Other values (1054)
1495 

Length

Max length54
Median length53
Mean length28.75308642
Min length9

Characters and Unicode

Total characters44251
Distinct characters103
Distinct categories11 ?
Distinct scripts6 ?
Distinct blocks6 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique750 ?
Unique (%)48.7%

Sample

1st row(568 notes)
2nd row83 % avis positifs (17,752 notes)
3rd row86 % avis positifs (295 notes)
4th row(23,832 notes)
5th row85 % avis positifs (14,482 notes)

Common Values

ValueCountFrequency (%)
83 % avis positifs (32,168 notes)13
 
0.8%
86 % avis positifs (12,309 notes)11
 
0.7%
87 % avis positifs (42,919 notes)8
 
0.5%
86 % avis positifs (65,189 notes)6
 
0.4%
89 % avis positifs (55,499 notes)6
 
0.4%
85 % avis positifs (80,093 notes)6
 
0.4%
84 % avis positifs (5,654 notes)6
 
0.4%
84 % avis positifs (36,361 notes)6
 
0.4%
82 % avis positifs (32,858 notes)5
 
0.3%
89 % avis positifs (246,312 notes)5
 
0.3%
Other values (1049)1467
95.3%

Length

2022-08-25T01:41:57.403934image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
notes1476
18.4%
avis1191
14.9%
positifs1191
14.9%
1191
14.9%
86138
 
1.7%
85126
 
1.6%
87112
 
1.4%
88111
 
1.4%
84101
 
1.3%
8995
 
1.2%
Other values (972)2279
28.4%

Most occurring characters

ValueCountFrequency (%)
6472
14.6%
s5142
 
11.6%
i3753
 
8.5%
t2754
 
6.2%
o2743
 
6.2%
81628
 
3.7%
e1612
 
3.6%
n1543
 
3.5%
(1538
 
3.5%
)1538
 
3.5%
Other values (93)15528
35.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter22973
51.9%
Decimal Number9038
 
20.4%
Space Separator6472
 
14.6%
Other Punctuation2501
 
5.7%
Open Punctuation1538
 
3.5%
Close Punctuation1538
 
3.5%
Other Letter123
 
0.3%
Uppercase Letter57
 
0.1%
Nonspacing Mark5
 
< 0.1%
Connector Punctuation3
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
s5142
22.4%
i3753
16.3%
t2754
12.0%
o2743
11.9%
e1612
 
7.0%
n1543
 
6.7%
a1301
 
5.7%
v1232
 
5.4%
p1221
 
5.3%
f1208
 
5.3%
Other values (32)464
 
2.0%
Other Letter
ValueCountFrequency (%)
ي12
 
9.8%
ف8
 
6.5%
ا8
 
6.5%
ت8
 
6.5%
د8
 
6.5%
و4
 
3.3%
ج4
 
3.3%
إ4
 
3.3%
4
 
3.3%
4
 
3.3%
Other values (25)59
48.0%
Decimal Number
ValueCountFrequency (%)
81628
18.0%
11128
12.5%
9913
10.1%
3876
9.7%
2867
9.6%
7773
8.6%
5770
8.5%
6734
8.1%
4698
7.7%
0651
 
7.2%
Uppercase Letter
ValueCountFrequency (%)
F33
57.9%
P18
31.6%
B4
 
7.0%
O1
 
1.8%
G1
 
1.8%
Other Punctuation
ValueCountFrequency (%)
,1252
50.1%
%1245
49.8%
:4
 
0.2%
Nonspacing Mark
ValueCountFrequency (%)
4
80.0%
1
 
20.0%
Spacing Mark
ValueCountFrequency (%)
2
66.7%
1
33.3%
Space Separator
ValueCountFrequency (%)
6472
100.0%
Open Punctuation
ValueCountFrequency (%)
(1538
100.0%
Close Punctuation
ValueCountFrequency (%)
)1538
100.0%
Connector Punctuation
ValueCountFrequency (%)
_3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin22949
51.9%
Common21090
47.7%
Arabic84
 
0.2%
Cyrillic81
 
0.2%
Thai36
 
0.1%
Khmer11
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
s5142
22.4%
i3753
16.4%
t2754
12.0%
o2743
12.0%
e1612
 
7.0%
n1543
 
6.7%
a1301
 
5.7%
v1232
 
5.4%
p1221
 
5.3%
f1208
 
5.3%
Other values (21)440
 
1.9%
Common
ValueCountFrequency (%)
6472
30.7%
81628
 
7.7%
(1538
 
7.3%
)1538
 
7.3%
,1252
 
5.9%
%1245
 
5.9%
11128
 
5.3%
9913
 
4.3%
3876
 
4.2%
2867
 
4.1%
Other values (7)3633
17.2%
Cyrillic
ValueCountFrequency (%)
о12
14.8%
т9
11.1%
ы6
 
7.4%
л6
 
7.4%
в6
 
7.4%
и6
 
7.4%
е6
 
7.4%
н6
 
7.4%
г3
 
3.7%
й3
 
3.7%
Other values (6)18
22.2%
Arabic
ValueCountFrequency (%)
ي12
14.3%
ف8
 
9.5%
ا8
 
9.5%
ت8
 
9.5%
د8
 
9.5%
و4
 
4.8%
ج4
 
4.8%
إ4
 
4.8%
ب4
 
4.8%
ع4
 
4.8%
Other values (5)20
23.8%
Thai
ValueCountFrequency (%)
4
11.1%
4
11.1%
4
11.1%
4
11.1%
2
 
5.6%
2
 
5.6%
2
 
5.6%
2
 
5.6%
2
 
5.6%
2
 
5.6%
Other values (4)8
22.2%
Khmer
ValueCountFrequency (%)
2
18.2%
1
9.1%
1
9.1%
1
9.1%
1
9.1%
1
9.1%
1
9.1%
1
9.1%
1
9.1%
1
9.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII44016
99.5%
Arabic84
 
0.2%
Cyrillic81
 
0.2%
Thai36
 
0.1%
None23
 
0.1%
Khmer11
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
6472
14.7%
s5142
 
11.7%
i3753
 
8.5%
t2754
 
6.3%
o2743
 
6.2%
81628
 
3.7%
e1612
 
3.7%
n1543
 
3.5%
(1538
 
3.5%
)1538
 
3.5%
Other values (35)15293
34.7%
Arabic
ValueCountFrequency (%)
ي12
14.3%
ف8
 
9.5%
ا8
 
9.5%
ت8
 
9.5%
د8
 
9.5%
و4
 
4.8%
ج4
 
4.8%
إ4
 
4.8%
ب4
 
4.8%
ع4
 
4.8%
Other values (5)20
23.8%
Cyrillic
ValueCountFrequency (%)
о12
14.8%
т9
11.1%
ы6
 
7.4%
л6
 
7.4%
в6
 
7.4%
и6
 
7.4%
е6
 
7.4%
н6
 
7.4%
г3
 
3.7%
й3
 
3.7%
Other values (6)18
22.2%
None
ValueCountFrequency (%)
õ9
39.1%
ç9
39.1%
ó5
21.7%
Thai
ValueCountFrequency (%)
4
11.1%
4
11.1%
4
11.1%
4
11.1%
2
 
5.6%
2
 
5.6%
2
 
5.6%
2
 
5.6%
2
 
5.6%
2
 
5.6%
Other values (4)8
22.2%
Khmer
ValueCountFrequency (%)
2
18.2%
1
9.1%
1
9.1%
1
9.1%
1
9.1%
1
9.1%
1
9.1%
1
9.1%
1
9.1%
1
9.1%

merchant_rating_count
Real number (ℝ≥0)

HIGH CORRELATION

Distinct917
Distinct (%)59.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26772.08512
Minimum0
Maximum2174765
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size12.1 KiB
2022-08-25T01:41:57.589948image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile88.9
Q12052.5
median8197
Q324564
95-th percentile105015
Maximum2174765
Range2174765
Interquartile range (IQR)22511.5

Descriptive statistics

Standard deviation79191.41272
Coefficient of variation (CV)2.957984496
Kurtosis374.4381396
Mean26772.08512
Median Absolute Deviation (MAD)7527
Skewness15.79151466
Sum41202239
Variance6271279849
MonotonicityNot monotonic
2022-08-25T01:41:57.746962image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3216814
 
0.9%
1230912
 
0.8%
429198
 
0.5%
106008
 
0.5%
881938
 
0.5%
800938
 
0.5%
554997
 
0.5%
177526
 
0.4%
651896
 
0.4%
56546
 
0.4%
Other values (907)1456
94.6%
ValueCountFrequency (%)
01
 
0.1%
32
0.1%
42
0.1%
64
0.3%
81
 
0.1%
91
 
0.1%
132
0.1%
171
 
0.1%
182
0.1%
213
0.2%
ValueCountFrequency (%)
21747651
 
0.1%
8398823
0.2%
4027433
0.2%
3668981
 
0.1%
3304051
 
0.1%
3200312
 
0.1%
2951611
 
0.1%
2532491
 
0.1%
2463125
0.3%
2309511
 
0.1%

merchant_rating
Real number (ℝ≥0)

HIGH CORRELATION

Distinct952
Distinct (%)61.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.034835989
Minimum2.333333333
Maximum5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.1 KiB
2022-08-25T01:41:57.955976image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum2.333333333
5-th percentile3.713646307
Q13.923267732
median4.045170201
Q34.165040009
95-th percentile4.325339738
Maximum5
Range2.666666667
Interquartile range (IQR)0.2417722777

Descriptive statistics

Standard deviation0.2033421338
Coefficient of variation (CV)0.05039662933
Kurtosis5.51984589
Mean4.034835989
Median Absolute Deviation (MAD)0.1208126416
Skewness-1.045650434
Sum6209.612587
Variance0.04134802337
MonotonicityNot monotonic
2022-08-25T01:41:58.149787image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.88454364614
 
0.9%
4.04517020112
 
0.8%
4.006692228
 
0.5%
4.0808907748
 
0.5%
4.1059670548
 
0.5%
3.867547178
 
0.5%
4.1388853857
 
0.5%
4.1214837436
 
0.4%
4.0496402776
 
0.4%
3.8996732766
 
0.4%
Other values (942)1456
94.6%
ValueCountFrequency (%)
2.3333333331
0.1%
2.9411764711
0.1%
31
0.1%
3.0344827591
0.1%
3.0389610391
0.1%
3.1860465122
0.1%
3.18751
0.1%
3.251
0.1%
3.2985074631
0.1%
3.3382899631
0.1%
ValueCountFrequency (%)
51
0.1%
4.577519381
0.1%
4.5218658891
0.1%
4.51251
0.1%
4.5014720311
0.1%
4.52
0.1%
4.4866310161
0.1%
4.4843966711
0.1%
4.4710175151
0.1%
4.4660194171
0.1%

merchant_id
Categorical

HIGH CARDINALITY

Distinct958
Distinct (%)62.2%
Missing0
Missing (%)0.0%
Memory size12.1 KiB
558c2cdc89d53c4005ea2920
 
14
5acaf29d5ebcfd72403106a8
 
12
582833faea77701b456c786a
 
8
564d8a9ac0f55a1276cd96f8
 
8
5533c83986ff95173dc017d0
 
8
Other values (953)
1489 

Length

Max length24
Median length24
Mean length24
Min length24

Characters and Unicode

Total characters36936
Distinct characters16
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique642 ?
Unique (%)41.7%

Sample

1st row595097d6a26f6e070cb878d1
2nd row56458aa03a698c35c9050988
3rd row5d464a1ffdf7bc44ee933c65
4th row58cfdefdacb37b556efdff7c
5th row5ab3b592c3911a095ad5dadb

Common Values

ValueCountFrequency (%)
558c2cdc89d53c4005ea292014
 
0.9%
5acaf29d5ebcfd72403106a812
 
0.8%
582833faea77701b456c786a8
 
0.5%
564d8a9ac0f55a1276cd96f88
 
0.5%
5533c83986ff95173dc017d08
 
0.5%
583138b06339b410ab9663ec8
 
0.5%
5926c5ace8ff5525241b368d7
 
0.5%
57b03628c676b3573ba2a0816
 
0.4%
56fba259138ef73c2a749a566
 
0.4%
5b160017daac45594728d9ba6
 
0.4%
Other values (948)1456
94.6%

Length

2022-08-25T01:41:58.325204image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
558c2cdc89d53c4005ea292014
 
0.9%
5acaf29d5ebcfd72403106a812
 
0.8%
582833faea77701b456c786a8
 
0.5%
564d8a9ac0f55a1276cd96f88
 
0.5%
5533c83986ff95173dc017d08
 
0.5%
583138b06339b410ab9663ec8
 
0.5%
5926c5ace8ff5525241b368d7
 
0.5%
583da4b58108913e6c79a32e6
 
0.4%
5639cd9bddb94f103070ef9f6
 
0.4%
55c8a4c33a698c6010edcd9e6
 
0.4%
Other values (948)1456
94.6%

Most occurring characters

ValueCountFrequency (%)
53927
 
10.6%
82396
 
6.5%
62384
 
6.5%
02372
 
6.4%
72317
 
6.3%
12285
 
6.2%
32268
 
6.1%
42214
 
6.0%
a2200
 
6.0%
c2169
 
5.9%
Other values (6)12404
33.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number24372
66.0%
Lowercase Letter12564
34.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
53927
16.1%
82396
9.8%
62384
9.8%
02372
9.7%
72317
9.5%
12285
9.4%
32268
9.3%
42214
9.1%
92131
8.7%
22078
8.5%
Lowercase Letter
ValueCountFrequency (%)
a2200
17.5%
c2169
17.3%
d2167
17.2%
e2104
16.7%
b2057
16.4%
f1867
14.9%

Most occurring scripts

ValueCountFrequency (%)
Common24372
66.0%
Latin12564
34.0%

Most frequent character per script

Common
ValueCountFrequency (%)
53927
16.1%
82396
9.8%
62384
9.8%
02372
9.7%
72317
9.5%
12285
9.4%
32268
9.3%
42214
9.1%
92131
8.7%
22078
8.5%
Latin
ValueCountFrequency (%)
a2200
17.5%
c2169
17.3%
d2167
17.2%
e2104
16.7%
b2057
16.4%
f1867
14.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII36936
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
53927
 
10.6%
82396
 
6.5%
62384
 
6.5%
02372
 
6.4%
72317
 
6.3%
12285
 
6.2%
32268
 
6.1%
42214
 
6.0%
a2200
 
6.0%
c2169
 
5.9%
Other values (6)12404
33.6%
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size12.1 KiB
0
1314 
1
225 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1539
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
01314
85.4%
1225
 
14.6%

Length

2022-08-25T01:41:58.470244image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-08-25T01:41:58.655258image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
01314
85.4%
1225
 
14.6%

Most occurring characters

ValueCountFrequency (%)
01314
85.4%
1225
 
14.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1539
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
01314
85.4%
1225
 
14.6%

Most occurring scripts

ValueCountFrequency (%)
Common1539
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
01314
85.4%
1225
 
14.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII1539
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
01314
85.4%
1225
 
14.6%

product_url
Categorical

HIGH CARDINALITY
UNIFORM

Distinct1341
Distinct (%)87.1%
Missing0
Missing (%)0.0%
Memory size12.1 KiB
https://www.wish.com/c/5cd3e32fd908537780580e43
 
2
https://www.wish.com/c/5ebbd192c1b69c3de34286cc
 
2
https://www.wish.com/c/5eb38309fa6f111ca122bed4
 
2
https://www.wish.com/c/5ec1ffa502f451526044f724
 
2
https://www.wish.com/c/5ed9de39b6befd33de8dd908
 
2
Other values (1336)
1529 

Length

Max length47
Median length47
Mean length47
Min length47

Characters and Unicode

Total characters72333
Distinct characters27
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1143 ?
Unique (%)74.3%

Sample

1st rowhttps://www.wish.com/c/5e9ae51d43d6a96e303acdb0
2nd rowhttps://www.wish.com/c/58940d436a0d3d5da4e95a38
3rd rowhttps://www.wish.com/c/5ea10e2c617580260d55310a
4th rowhttps://www.wish.com/c/5cedf17ad1d44c52c59e4aca
5th rowhttps://www.wish.com/c/5ebf5819ebac372b070b0e70

Common Values

ValueCountFrequency (%)
https://www.wish.com/c/5cd3e32fd908537780580e432
 
0.1%
https://www.wish.com/c/5ebbd192c1b69c3de34286cc2
 
0.1%
https://www.wish.com/c/5eb38309fa6f111ca122bed42
 
0.1%
https://www.wish.com/c/5ec1ffa502f451526044f7242
 
0.1%
https://www.wish.com/c/5ed9de39b6befd33de8dd9082
 
0.1%
https://www.wish.com/c/5cee1ddd050d347f75e6668e2
 
0.1%
https://www.wish.com/c/5cabfcdc32227053f6254ee62
 
0.1%
https://www.wish.com/c/5e943811e6cd6f1680c644142
 
0.1%
https://www.wish.com/c/5edf3082e066d71501a40c732
 
0.1%
https://www.wish.com/c/5df202bb707508021388614a2
 
0.1%
Other values (1331)1519
98.7%

Length

2022-08-25T01:41:58.823272image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
https://www.wish.com/c/5cd3e32fd908537780580e432
 
0.1%
https://www.wish.com/c/5e12c01191235958ecdd62852
 
0.1%
https://www.wish.com/c/5e9dad8cbc19c300417e17332
 
0.1%
https://www.wish.com/c/5b6021aee07ac6197df2d0972
 
0.1%
https://www.wish.com/c/5eb63e65f98a3634f4d430b32
 
0.1%
https://www.wish.com/c/5d8d8f2e0c400f2f97671e302
 
0.1%
https://www.wish.com/c/5ca084765e18c31890903f2a2
 
0.1%
https://www.wish.com/c/5cc01186416a307424603aba2
 
0.1%
https://www.wish.com/c/5ec39565bb9e684ea7371e7c2
 
0.1%
https://www.wish.com/c/5d03176da8c15d5e4b5171e42
 
0.1%
Other values (1331)1519
98.7%

Most occurring characters

ValueCountFrequency (%)
/6156
 
8.5%
w6156
 
8.5%
c5485
 
7.6%
53612
 
5.0%
h3078
 
4.3%
s3078
 
4.3%
.3078
 
4.3%
t3078
 
4.3%
e2714
 
3.8%
02380
 
3.3%
Other values (17)33518
46.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter38301
53.0%
Decimal Number23259
32.2%
Other Punctuation10773
 
14.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
w6156
16.1%
c5485
14.3%
h3078
8.0%
s3078
8.0%
t3078
8.0%
e2714
7.1%
d2291
 
6.0%
b2257
 
5.9%
a2059
 
5.4%
f1949
 
5.1%
Other values (4)6156
16.1%
Decimal Number
ValueCountFrequency (%)
53612
15.5%
02380
10.2%
12251
9.7%
22232
9.6%
42194
9.4%
32133
9.2%
92130
9.2%
72130
9.2%
62115
9.1%
82082
9.0%
Other Punctuation
ValueCountFrequency (%)
/6156
57.1%
.3078
28.6%
:1539
 
14.3%

Most occurring scripts

ValueCountFrequency (%)
Latin38301
53.0%
Common34032
47.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
w6156
16.1%
c5485
14.3%
h3078
8.0%
s3078
8.0%
t3078
8.0%
e2714
7.1%
d2291
 
6.0%
b2257
 
5.9%
a2059
 
5.4%
f1949
 
5.1%
Other values (4)6156
16.1%
Common
ValueCountFrequency (%)
/6156
18.1%
53612
10.6%
.3078
9.0%
02380
 
7.0%
12251
 
6.6%
22232
 
6.6%
42194
 
6.4%
32133
 
6.3%
92130
 
6.3%
72130
 
6.3%
Other values (3)5736
16.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII72333
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
/6156
 
8.5%
w6156
 
8.5%
c5485
 
7.6%
53612
 
5.0%
h3078
 
4.3%
s3078
 
4.3%
.3078
 
4.3%
t3078
 
4.3%
e2714
 
3.8%
02380
 
3.3%
Other values (17)33518
46.3%

product_picture
Categorical

HIGH CARDINALITY
UNIFORM

Distinct1341
Distinct (%)87.1%
Missing0
Missing (%)0.0%
Memory size12.1 KiB
https://contestimg.wish.com/api/webimage/5cd3e32fd908537780580e43-medium.jpg
 
2
https://contestimg.wish.com/api/webimage/5ebbd192c1b69c3de34286cc-medium.jpg
 
2
https://contestimg.wish.com/api/webimage/5eb38309fa6f111ca122bed4-medium.jpg
 
2
https://contestimg.wish.com/api/webimage/5ec1ffa502f451526044f724-medium.jpg
 
2
https://contestimg.wish.com/api/webimage/5ed9de39b6befd33de8dd908-medium.jpg
 
2
Other values (1336)
1529 

Length

Max length76
Median length76
Mean length76
Min length76

Characters and Unicode

Total characters116964
Distinct characters32
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1143 ?
Unique (%)74.3%

Sample

1st rowhttps://contestimg.wish.com/api/webimage/5e9ae51d43d6a96e303acdb0-medium.jpg
2nd rowhttps://contestimg.wish.com/api/webimage/58940d436a0d3d5da4e95a38-medium.jpg
3rd rowhttps://contestimg.wish.com/api/webimage/5ea10e2c617580260d55310a-medium.jpg
4th rowhttps://contestimg.wish.com/api/webimage/5cedf17ad1d44c52c59e4aca-medium.jpg
5th rowhttps://contestimg.wish.com/api/webimage/5ebf5819ebac372b070b0e70-medium.jpg

Common Values

ValueCountFrequency (%)
https://contestimg.wish.com/api/webimage/5cd3e32fd908537780580e43-medium.jpg2
 
0.1%
https://contestimg.wish.com/api/webimage/5ebbd192c1b69c3de34286cc-medium.jpg2
 
0.1%
https://contestimg.wish.com/api/webimage/5eb38309fa6f111ca122bed4-medium.jpg2
 
0.1%
https://contestimg.wish.com/api/webimage/5ec1ffa502f451526044f724-medium.jpg2
 
0.1%
https://contestimg.wish.com/api/webimage/5ed9de39b6befd33de8dd908-medium.jpg2
 
0.1%
https://contestimg.wish.com/api/webimage/5cee1ddd050d347f75e6668e-medium.jpg2
 
0.1%
https://contestimg.wish.com/api/webimage/5cabfcdc32227053f6254ee6-medium.jpg2
 
0.1%
https://contestimg.wish.com/api/webimage/5e943811e6cd6f1680c64414-medium.jpg2
 
0.1%
https://contestimg.wish.com/api/webimage/5edf3082e066d71501a40c73-medium.jpg2
 
0.1%
https://contestimg.wish.com/api/webimage/5df202bb707508021388614a-medium.jpg2
 
0.1%
Other values (1331)1519
98.7%

Length

2022-08-25T01:41:59.050290image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
https://contestimg.wish.com/api/webimage/5cd3e32fd908537780580e43-medium.jpg2
 
0.1%
https://contestimg.wish.com/api/webimage/5e12c01191235958ecdd6285-medium.jpg2
 
0.1%
https://contestimg.wish.com/api/webimage/5e9dad8cbc19c300417e1733-medium.jpg2
 
0.1%
https://contestimg.wish.com/api/webimage/5b6021aee07ac6197df2d097-medium.jpg2
 
0.1%
https://contestimg.wish.com/api/webimage/5eb63e65f98a3634f4d430b3-medium.jpg2
 
0.1%
https://contestimg.wish.com/api/webimage/5d8d8f2e0c400f2f97671e30-medium.jpg2
 
0.1%
https://contestimg.wish.com/api/webimage/5ca084765e18c31890903f2a-medium.jpg2
 
0.1%
https://contestimg.wish.com/api/webimage/5cc01186416a307424603aba-medium.jpg2
 
0.1%
https://contestimg.wish.com/api/webimage/5ec39565bb9e684ea7371e7c-medium.jpg2
 
0.1%
https://contestimg.wish.com/api/webimage/5d03176da8c15d5e4b5171e4-medium.jpg2
 
0.1%
Other values (1331)1519
98.7%

Most occurring characters

ValueCountFrequency (%)
e8870
 
7.6%
/7695
 
6.6%
i7695
 
6.6%
m7695
 
6.6%
t6156
 
5.3%
c5485
 
4.7%
a5137
 
4.4%
p4617
 
3.9%
s4617
 
3.9%
g4617
 
3.9%
Other values (22)54380
46.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter78315
67.0%
Decimal Number23259
 
19.9%
Other Punctuation13851
 
11.8%
Dash Punctuation1539
 
1.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e8870
11.3%
i7695
 
9.8%
m7695
 
9.8%
t6156
 
7.9%
c5485
 
7.0%
a5137
 
6.6%
p4617
 
5.9%
s4617
 
5.9%
g4617
 
5.9%
d3830
 
4.9%
Other values (8)19596
25.0%
Decimal Number
ValueCountFrequency (%)
53612
15.5%
02380
10.2%
12251
9.7%
22232
9.6%
42194
9.4%
32133
9.2%
92130
9.2%
72130
9.2%
62115
9.1%
82082
9.0%
Other Punctuation
ValueCountFrequency (%)
/7695
55.6%
.4617
33.3%
:1539
 
11.1%
Dash Punctuation
ValueCountFrequency (%)
-1539
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin78315
67.0%
Common38649
33.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e8870
11.3%
i7695
 
9.8%
m7695
 
9.8%
t6156
 
7.9%
c5485
 
7.0%
a5137
 
6.6%
p4617
 
5.9%
s4617
 
5.9%
g4617
 
5.9%
d3830
 
4.9%
Other values (8)19596
25.0%
Common
ValueCountFrequency (%)
/7695
19.9%
.4617
11.9%
53612
9.3%
02380
 
6.2%
12251
 
5.8%
22232
 
5.8%
42194
 
5.7%
32133
 
5.5%
92130
 
5.5%
72130
 
5.5%
Other values (4)7275
18.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII116964
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e8870
 
7.6%
/7695
 
6.6%
i7695
 
6.6%
m7695
 
6.6%
t6156
 
5.3%
c5485
 
4.7%
a5137
 
4.4%
p4617
 
3.9%
s4617
 
3.9%
g4617
 
3.9%
Other values (22)54380
46.5%

product_id
Categorical

HIGH CARDINALITY
UNIFORM

Distinct1341
Distinct (%)87.1%
Missing0
Missing (%)0.0%
Memory size12.1 KiB
5cd3e32fd908537780580e43
 
2
5ebbd192c1b69c3de34286cc
 
2
5eb38309fa6f111ca122bed4
 
2
5ec1ffa502f451526044f724
 
2
5ed9de39b6befd33de8dd908
 
2
Other values (1336)
1529 

Length

Max length24
Median length24
Mean length24
Min length24

Characters and Unicode

Total characters36936
Distinct characters16
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1143 ?
Unique (%)74.3%

Sample

1st row5e9ae51d43d6a96e303acdb0
2nd row58940d436a0d3d5da4e95a38
3rd row5ea10e2c617580260d55310a
4th row5cedf17ad1d44c52c59e4aca
5th row5ebf5819ebac372b070b0e70

Common Values

ValueCountFrequency (%)
5cd3e32fd908537780580e432
 
0.1%
5ebbd192c1b69c3de34286cc2
 
0.1%
5eb38309fa6f111ca122bed42
 
0.1%
5ec1ffa502f451526044f7242
 
0.1%
5ed9de39b6befd33de8dd9082
 
0.1%
5cee1ddd050d347f75e6668e2
 
0.1%
5cabfcdc32227053f6254ee62
 
0.1%
5e943811e6cd6f1680c644142
 
0.1%
5edf3082e066d71501a40c732
 
0.1%
5df202bb707508021388614a2
 
0.1%
Other values (1331)1519
98.7%

Length

2022-08-25T01:41:59.195487image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
5cd3e32fd908537780580e432
 
0.1%
5e12c01191235958ecdd62852
 
0.1%
5e9dad8cbc19c300417e17332
 
0.1%
5b6021aee07ac6197df2d0972
 
0.1%
5eb63e65f98a3634f4d430b32
 
0.1%
5d8d8f2e0c400f2f97671e302
 
0.1%
5ca084765e18c31890903f2a2
 
0.1%
5cc01186416a307424603aba2
 
0.1%
5ec39565bb9e684ea7371e7c2
 
0.1%
5d03176da8c15d5e4b5171e42
 
0.1%
Other values (1331)1519
98.7%

Most occurring characters

ValueCountFrequency (%)
53612
 
9.8%
e2714
 
7.3%
c2407
 
6.5%
02380
 
6.4%
d2291
 
6.2%
b2257
 
6.1%
12251
 
6.1%
22232
 
6.0%
42194
 
5.9%
32133
 
5.8%
Other values (6)12465
33.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number23259
63.0%
Lowercase Letter13677
37.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
53612
15.5%
02380
10.2%
12251
9.7%
22232
9.6%
42194
9.4%
32133
9.2%
92130
9.2%
72130
9.2%
62115
9.1%
82082
9.0%
Lowercase Letter
ValueCountFrequency (%)
e2714
19.8%
c2407
17.6%
d2291
16.8%
b2257
16.5%
a2059
15.1%
f1949
14.3%

Most occurring scripts

ValueCountFrequency (%)
Common23259
63.0%
Latin13677
37.0%

Most frequent character per script

Common
ValueCountFrequency (%)
53612
15.5%
02380
10.2%
12251
9.7%
22232
9.6%
42194
9.4%
32133
9.2%
92130
9.2%
72130
9.2%
62115
9.1%
82082
9.0%
Latin
ValueCountFrequency (%)
e2714
19.8%
c2407
17.6%
d2291
16.8%
b2257
16.5%
a2059
15.1%
f1949
14.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII36936
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
53612
 
9.8%
e2714
 
7.3%
c2407
 
6.5%
02380
 
6.4%
d2291
 
6.2%
b2257
 
6.1%
12251
 
6.1%
22232
 
6.0%
42194
 
5.9%
32133
 
5.8%
Other values (6)12465
33.7%

num_listings
Real number (ℝ≥0)

Distinct10
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.498375569
Minimum1
Maximum14
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.1 KiB
2022-08-25T01:41:59.323193image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q33
95-th percentile6
Maximum14
Range13
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.125874094
Coefficient of variation (CV)0.8509025306
Kurtosis9.395458637
Mean2.498375569
Median Absolute Deviation (MAD)1
Skewness2.629782527
Sum3845
Variance4.519340662
MonotonicityNot monotonic
2022-08-25T01:41:59.451204image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1642
41.7%
2374
24.3%
4180
 
11.7%
3171
 
11.1%
565
 
4.2%
642
 
2.7%
832
 
2.1%
1414
 
0.9%
1212
 
0.8%
77
 
0.5%
ValueCountFrequency (%)
1642
41.7%
2374
24.3%
3171
 
11.1%
4180
 
11.7%
565
 
4.2%
642
 
2.7%
77
 
0.5%
832
 
2.1%
1212
 
0.8%
1414
 
0.9%
ValueCountFrequency (%)
1414
 
0.9%
1212
 
0.8%
832
 
2.1%
77
 
0.5%
642
 
2.7%
565
 
4.2%
4180
 
11.7%
3171
 
11.1%
2374
24.3%
1642
41.7%

tag_list
Categorical

HIGH CARDINALITY
UNIFORM

Distinct1230
Distinct (%)79.9%
Missing0
Missing (%)0.0%
Memory size12.1 KiB
['Summer', 'Fashion', 'Necks', 'Skirts', 'Dress', 'Loose', "Women's Fashion", 'Round neck', 'beach dress', 'sleeveless', 'Beach', 'Casual', 'Women']
 
15
['Summer', 'Sling', 'Dresses', 'Dress', 'V-neck', 'Casual', 'Pocket', "Women's Fashion", 'Sleeveless dress', 'women dress', 'Floral', 'sleeveless', 'Women', 'loose dress', 'Pleated', 'casual dress']
 
9
['slimming', 'wasitcincher', 'Fashion', 'waistgirdle', 'slimmingcorset', 'Corset', 'Summer', 'Waist', 'waist trainer', 'Fashion Accessory', 'Vest', 'shaperwear', 'belt']
 
8
['Summer', 'Fashion', 'Necks', 'Beach', 'Dress', 'Loose', 'beach dress', 'Round neck', "Women's Fashion", 'sleeveless', 'Skirts', 'Casual', 'Women']
 
7
['Summer', 'Women Rompers', 'Plus Size', 'women long pants', 'linenjumpsuit', 'pants', 'Overalls', 'Loose', 'plussizejumpsuit', "Women's Fashion", 'strappant', 'Long pants', 'Jumpsuits & Rompers', 'rompers womens jumpsuit', 'Vintage', 'Women', 'women Jumpsuit', 'Casual', 'jumpsuit']
 
6
Other values (1225)
1494 

Length

Max length512
Median length311
Mean length222.5640026
Min length86

Characters and Unicode

Total characters342526
Distinct characters88
Distinct categories10 ?
Distinct scripts5 ?
Distinct blocks5 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1014 ?
Unique (%)65.9%

Sample

1st row['Summer', 'Fashion', 'womenunderwearsuit', 'printedpajamasset', 'womencasualshort', "Women's Fashion", 'flamingo', 'loungewearset', 'Casual', 'Shirt', 'casualsleepwear', 'Shorts', 'flamingotshirt', 'Elastic', 'Vintage', 'Tops', 'tshirtandshortsset', 'Women', 'Sleepwear', 'Print', 'womenpajamasset', 'womennightwear', 'Pajamas', 'womensleepwearset']
2nd row['Mini', 'womens dresses', 'Summer', 'Patchwork', 'fashion dress', 'Dress', 'Mini dress', "Women's Fashion", 'Women S Clothing', 'backless', 'party', 'summer dresses', 'sleeveless', 'sexy', 'Casual']
3rd row['Summer', 'cardigan', 'women beachwear', 'chiffon', 'Sexy women', 'Coat', 'summercardigan', 'openfront', 'short sleeves', 'Swimsuit', "Women's Fashion", 'leaf', 'Green', 'printed', 'Spring', 'longcardigan', 'Women', 'Beach', 'kimono']
4th row['Summer', 'Shorts', 'Cotton', 'Cotton T Shirt', 'Sleeve', 'printedletterstop', 'Clothing', 'Tops', 'Necks', 'short sleeves', "Women's Fashion", 'Women Clothing', 'printed', 'Women', 'tshirtforwomen', 'Fashion', 'T Shirts', 'Shirt']
5th row['Summer', 'Plus Size', 'Lace', 'Casual pants', 'Bottom', 'pants', 'Loose', "Women's Fashion", 'Shorts', 'Lace Up', 'Elastic', 'Casual', 'Women']

Common Values

ValueCountFrequency (%)
['Summer', 'Fashion', 'Necks', 'Skirts', 'Dress', 'Loose', "Women's Fashion", 'Round neck', 'beach dress', 'sleeveless', 'Beach', 'Casual', 'Women']15
 
1.0%
['Summer', 'Sling', 'Dresses', 'Dress', 'V-neck', 'Casual', 'Pocket', "Women's Fashion", 'Sleeveless dress', 'women dress', 'Floral', 'sleeveless', 'Women', 'loose dress', 'Pleated', 'casual dress']9
 
0.6%
['slimming', 'wasitcincher', 'Fashion', 'waistgirdle', 'slimmingcorset', 'Corset', 'Summer', 'Waist', 'waist trainer', 'Fashion Accessory', 'Vest', 'shaperwear', 'belt']8
 
0.5%
['Summer', 'Fashion', 'Necks', 'Beach', 'Dress', 'Loose', 'beach dress', 'Round neck', "Women's Fashion", 'sleeveless', 'Skirts', 'Casual', 'Women']7
 
0.5%
['Summer', 'Women Rompers', 'Plus Size', 'women long pants', 'linenjumpsuit', 'pants', 'Overalls', 'Loose', 'plussizejumpsuit', "Women's Fashion", 'strappant', 'Long pants', 'Jumpsuits & Rompers', 'rompers womens jumpsuit', 'Vintage', 'Women', 'women Jumpsuit', 'Casual', 'jumpsuit']6
 
0.4%
['Summer', 'Leggings', 'Fashion', 'high waist', 'pants', 'slim', "Women's Fashion", 'trousers', 'Green', 'Army', 'Women']6
 
0.4%
['Summer', 'short sleeve dress', 'neck dress', 'Necks', 'Sleeve', 'Beach', 'Dress', 'Loose', 'short sleeves', 'V-neck', 'Shorts', 'beach dress', 'Plus Size', 'Midi Dress', 'summer dress', 'Print', 'Pullovers', "Women's Fashion", 'Casual', 'Women']6
 
0.4%
['pajamaset', 'Fashion', 'sexy pajamas for womens', 'silksleepwearforwomen', 'silksleepwear', 'pajamassuit', 'Casual', "Women's Fashion", 'Summer', 'Sleepwear', 'pajamasforwomen', 'silksleepwearnightgown', 'silk', 'pajamassleepwear', "women's pajamas", 'Women']5
 
0.3%
['Summer', 'Shorts', 'high waist shorts', 'high waist', 'Casual pants', 'pants', 'summer shorts', 'Waist', 'Slim Fit', 'Short pants', "Women's Fashion", 'Plus Size', 'Lace Up', 'Women', 'Fashion', 'Casual', 'Lace']5
 
0.3%
['Mini', 'womens dresses', 'Summer', 'sleevele', 'Dress', 'Mini dress', "Women's Fashion", 'Fashion', 'backless', 'party', 'sexy', 'summer dresses', 'Women S Clothing', 'Casual', 'sleeveless']5
 
0.3%
Other values (1220)1467
95.3%

Length

2022-08-25T01:41:59.615288image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
fashion2761
 
7.4%
dress2659
 
7.1%
women1919
 
5.2%
summer1828
 
4.9%
women's1336
 
3.6%
casual1193
 
3.2%
tops815
 
2.2%
sleeveless809
 
2.2%
shorts730
 
2.0%
size696
 
1.9%
Other values (2097)22490
60.4%

Most occurring characters

ValueCountFrequency (%)
'52148
15.2%
35704
 
10.4%
s28513
 
8.3%
e25958
 
7.6%
,25251
 
7.4%
o16404
 
4.8%
r13699
 
4.0%
n13594
 
4.0%
i13353
 
3.9%
a12965
 
3.8%
Other values (78)104937
30.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter200773
58.6%
Other Punctuation80451
23.5%
Space Separator35704
 
10.4%
Uppercase Letter21810
 
6.4%
Close Punctuation1539
 
0.4%
Open Punctuation1539
 
0.4%
Dash Punctuation621
 
0.2%
Decimal Number80
 
< 0.1%
Other Letter5
 
< 0.1%
Connector Punctuation4
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
s28513
14.2%
e25958
12.9%
o16404
 
8.2%
r13699
 
6.8%
n13594
 
6.8%
i13353
 
6.7%
a12965
 
6.5%
t12578
 
6.3%
m10037
 
5.0%
l9390
 
4.7%
Other values (29)44282
22.1%
Uppercase Letter
ValueCountFrequency (%)
S5431
24.9%
F3277
15.0%
W2808
12.9%
C1700
 
7.8%
T1534
 
7.0%
P1457
 
6.7%
D1228
 
5.6%
L869
 
4.0%
B774
 
3.5%
V629
 
2.9%
Other values (14)2103
 
9.6%
Decimal Number
ValueCountFrequency (%)
225
31.2%
319
23.8%
49
 
11.2%
09
 
11.2%
18
 
10.0%
53
 
3.8%
93
 
3.8%
72
 
2.5%
82
 
2.5%
Other Punctuation
ValueCountFrequency (%)
'52148
64.8%
,25251
31.4%
"2864
 
3.6%
&165
 
0.2%
#21
 
< 0.1%
/2
 
< 0.1%
Other Letter
ValueCountFrequency (%)
1
20.0%
1
20.0%
1
20.0%
1
20.0%
1
20.0%
Space Separator
ValueCountFrequency (%)
35704
100.0%
Close Punctuation
ValueCountFrequency (%)
]1539
100.0%
Open Punctuation
ValueCountFrequency (%)
[1539
100.0%
Dash Punctuation
ValueCountFrequency (%)
-621
100.0%
Connector Punctuation
ValueCountFrequency (%)
_4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin222571
65.0%
Common119938
35.0%
Cyrillic12
 
< 0.1%
Han4
 
< 0.1%
Hiragana1
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
s28513
12.8%
e25958
 
11.7%
o16404
 
7.4%
r13699
 
6.2%
n13594
 
6.1%
i13353
 
6.0%
a12965
 
5.8%
t12578
 
5.7%
m10037
 
4.5%
l9390
 
4.2%
Other values (41)66080
29.7%
Common
ValueCountFrequency (%)
'52148
43.5%
35704
29.8%
,25251
21.1%
"2864
 
2.4%
]1539
 
1.3%
[1539
 
1.3%
-621
 
0.5%
&165
 
0.1%
225
 
< 0.1%
#21
 
< 0.1%
Other values (10)61
 
0.1%
Cyrillic
ValueCountFrequency (%)
т1
8.3%
и1
8.3%
к1
8.3%
с1
8.3%
ж1
8.3%
у1
8.3%
м1
8.3%
ы1
8.3%
о1
8.3%
р1
8.3%
Other values (2)2
16.7%
Han
ValueCountFrequency (%)
1
25.0%
1
25.0%
1
25.0%
1
25.0%
Hiragana
ValueCountFrequency (%)
1
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII342508
> 99.9%
Cyrillic12
 
< 0.1%
CJK4
 
< 0.1%
None1
 
< 0.1%
Hiragana1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
'52148
15.2%
35704
 
10.4%
s28513
 
8.3%
e25958
 
7.6%
,25251
 
7.4%
o16404
 
4.8%
r13699
 
4.0%
n13594
 
4.0%
i13353
 
3.9%
a12965
 
3.8%
Other values (60)104919
30.6%
Cyrillic
ValueCountFrequency (%)
т1
8.3%
и1
8.3%
к1
8.3%
с1
8.3%
ж1
8.3%
у1
8.3%
м1
8.3%
ы1
8.3%
о1
8.3%
р1
8.3%
Other values (2)2
16.7%
None
ValueCountFrequency (%)
é1
100.0%
CJK
ValueCountFrequency (%)
1
25.0%
1
25.0%
1
25.0%
1
25.0%
Hiragana
ValueCountFrequency (%)
1
100.0%

Interactions

2022-08-25T01:41:42.750773image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
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2022-08-25T01:41:25.397242image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T01:41:27.681855image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T01:41:29.979230image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T01:41:32.354514image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T01:41:35.142462image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T01:41:37.949677image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T01:41:40.760617image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T01:41:43.933398image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T01:40:59.848048image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T01:41:06.344686image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T01:41:08.573701image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T01:41:10.774024image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T01:41:13.009042image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T01:41:15.274824image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T01:41:17.513162image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T01:41:19.791307image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T01:41:22.240240image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T01:41:25.524236image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T01:41:27.806864image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T01:41:30.108239image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T01:41:32.482525image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T01:41:35.285468image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T01:41:38.083683image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T01:41:40.919631image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T01:41:44.084850image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T01:41:00.071063image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T01:41:06.475348image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T01:41:08.692588image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T01:41:10.903037image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T01:41:13.127055image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T01:41:15.404262image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T01:41:17.642144image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T01:41:19.915317image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T01:41:22.378248image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T01:41:25.679261image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T01:41:27.955557image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T01:41:30.231247image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T01:41:32.609535image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T01:41:35.448483image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T01:41:38.226694image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T01:41:41.061638image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T01:41:44.297558image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T01:41:00.271079image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T01:41:06.617360image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T01:41:08.822600image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T01:41:11.044045image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T01:41:13.265982image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T01:41:15.560939image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T01:41:17.782219image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T01:41:20.055058image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T01:41:22.531573image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T01:41:25.820529image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T01:41:28.098567image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T01:41:30.371259image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T01:41:32.767548image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T01:41:35.603493image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T01:41:38.415711image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T01:41:41.224652image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T01:41:44.505573image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T01:41:00.475095image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T01:41:06.760391image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T01:41:08.946606image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T01:41:11.178056image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T01:41:13.391994image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T01:41:15.698455image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T01:41:17.907792image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T01:41:20.198369image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T01:41:22.678587image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T01:41:25.943542image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T01:41:28.228577image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T01:41:30.496515image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T01:41:32.898554image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T01:41:35.758511image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T01:41:38.575722image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T01:41:41.429667image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T01:41:44.672588image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T01:41:00.637913image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T01:41:06.885598image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T01:41:09.073811image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T01:41:11.320873image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T01:41:13.522496image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T01:41:15.834531image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T01:41:18.035384image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T01:41:20.326977image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T01:41:22.816580image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T01:41:26.075551image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T01:41:28.361588image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T01:41:30.635528image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T01:41:33.033296image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T01:41:35.927517image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T01:41:38.748737image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T01:41:41.646687image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T01:41:44.852603image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T01:41:00.788922image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T01:41:07.016606image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T01:41:09.202820image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T01:41:11.451029image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T01:41:13.653507image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T01:41:15.973053image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T01:41:18.166850image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T01:41:20.451424image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T01:41:22.980592image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T01:41:26.203563image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T01:41:28.494597image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T01:41:30.768004image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T01:41:33.169308image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T01:41:36.077529image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T01:41:38.908476image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T01:41:41.827696image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T01:41:45.009819image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T01:41:00.956935image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T01:41:07.166731image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T01:41:09.332826image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T01:41:11.588868image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T01:41:13.784517image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T01:41:16.117352image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T01:41:18.305260image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T01:41:20.596761image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T01:41:23.131603image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T01:41:26.342572image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T01:41:28.647608image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T01:41:30.913016image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T01:41:33.329320image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T01:41:36.270544image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T01:41:39.083487image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T01:41:42.027716image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T01:41:45.151830image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T01:41:01.108840image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T01:41:07.286961image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T01:41:09.453836image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T01:41:11.723220image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T01:41:13.922953image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T01:41:16.249877image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T01:41:18.431711image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T01:41:20.737004image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T01:41:24.154699image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T01:41:26.469579image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T01:41:28.775619image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T01:41:31.056521image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T01:41:33.465330image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T01:41:36.481560image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T01:41:39.242502image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T01:41:42.210727image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T01:41:45.331844image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T01:41:01.276726image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T01:41:07.422862image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T01:41:09.575481image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T01:41:11.855083image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T01:41:14.042963image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T01:41:16.374886image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T01:41:18.573419image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T01:41:20.861015image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T01:41:24.294710image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T01:41:26.603594image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T01:41:28.898629image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T01:41:31.188532image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T01:41:33.604339image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T01:41:36.684576image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T01:41:39.415512image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T01:41:42.395743image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T01:41:45.497752image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T01:41:01.481742image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T01:41:07.548876image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T01:41:09.717963image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T01:41:11.998086image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T01:41:14.170139image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T01:41:16.504894image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T01:41:18.701501image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T01:41:20.997407image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T01:41:24.434634image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T01:41:26.741031image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T01:41:29.027640image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T01:41:31.325540image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T01:41:33.743352image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T01:41:36.848589image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T01:41:39.590527image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T01:41:42.579755image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Correlations

2022-08-25T01:41:59.785007image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-08-25T01:42:00.183733image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-08-25T01:42:00.644204image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-08-25T01:42:01.014894image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-08-25T01:42:01.269713image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-08-25T01:41:46.051795image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
A simple visualization of nullity by column.
2022-08-25T01:41:47.356749image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

titletitle_origpriceretail_priceunits_solduses_ad_boostsratingrating_countrating_five_countrating_four_countrating_three_countrating_two_countrating_one_countbadges_countbadge_local_productbadge_product_qualitybadge_fast_shippingtagsproduct_colorproduct_variation_size_idproduct_variation_inventoryshipping_option_priceshipping_is_expresscountries_shipped_toinventory_totalhas_urgency_bannerorigin_countrymerchant_titlemerchant_namemerchant_info_subtitlemerchant_rating_countmerchant_ratingmerchant_idmerchant_has_profile_pictureproduct_urlproduct_pictureproduct_idnum_listingstag_list
02020 Summer Vintage Flamingo Print Pajamas Set Casual Loose T Shirt Top And Elastic Shorts Women Sleepwear Night Wear Loungewear Sets2020 Summer Vintage Flamingo Print Pajamas Set Casual Loose T Shirt Top And Elastic Shorts Women Sleepwear Night Wear Loungewear Sets16.001410003.765426.08.08.01.09.00000Summer,Fashion,womenunderwearsuit,printedpajamasset,womencasualshort,Women's Fashion,flamingo,loungewearset,Casual,Shirt,casualsleepwear,Shorts,flamingotshirt,Elastic,Vintage,Tops,tshirtandshortsset,Women,Sleepwear,Print,womenpajamasset,womennightwear,Pajamas,womensleepwearsetwhiteM504034501CNzgrdejiazgrdejia(568 notes)5684.128521595097d6a26f6e070cb878d10https://www.wish.com/c/5e9ae51d43d6a96e303acdb0https://contestimg.wish.com/api/webimage/5e9ae51d43d6a96e303acdb0-medium.jpg5e9ae51d43d6a96e303acdb01['Summer', 'Fashion', 'womenunderwearsuit', 'printedpajamasset', 'womencasualshort', "Women's Fashion", 'flamingo', 'loungewearset', 'Casual', 'Shirt', 'casualsleepwear', 'Shorts', 'flamingotshirt', 'Elastic', 'Vintage', 'Tops', 'tshirtandshortsset', 'Women', 'Sleepwear', 'Print', 'womenpajamasset', 'womennightwear', 'Pajamas', 'womensleepwearset']
1SSHOUSE Summer Casual Sleeveless Soirée Party Soirée sans manches Vêtements de plage sexy Mini robe femme wshC1612242400387A21Women's Casual Summer Sleeveless Sexy Mini Dress8.00222000013.4561352269.01027.01027.0644.01077.00000Mini,womens dresses,Summer,Patchwork,fashion dress,Dress,Mini dress,Women's Fashion,Women S Clothing,backless,party,summer dresses,sleeveless,sexy,CasualgreenXS502041501CNSaraHousesarahouse83 % avis positifs (17,752 notes)177523.89967356458aa03a698c35c90509880https://www.wish.com/c/58940d436a0d3d5da4e95a38https://contestimg.wish.com/api/webimage/58940d436a0d3d5da4e95a38-medium.jpg58940d436a0d3d5da4e95a386['Mini', 'womens dresses', 'Summer', 'Patchwork', 'fashion dress', 'Dress', 'Mini dress', "Women's Fashion", 'Women S Clothing', 'backless', 'party', 'summer dresses', 'sleeveless', 'sexy', 'Casual']
22020 Nouvelle Arrivée Femmes Printemps et Été Plage Porter Longue Mince Cardigan Ouvert Avant Kimono Vert Feuille Imprimé En Mousseline de Soie Cardigan S-5XL2020 New Arrival Women Spring and Summer Beach Wear Long Thin Cardigan Open Front Kimono Green Leaf Printed Chiffon Cardigan S-5XL8.004310003.57145.04.04.00.03.00000Summer,cardigan,women beachwear,chiffon,Sexy women,Coat,summercardigan,openfront,short sleeves,Swimsuit,Women's Fashion,leaf,Green,printed,Spring,longcardigan,Women,Beach,kimonoleopardprintXS13036501CNhxt520hxt52086 % avis positifs (295 notes)2953.9898315d464a1ffdf7bc44ee933c650https://www.wish.com/c/5ea10e2c617580260d55310ahttps://contestimg.wish.com/api/webimage/5ea10e2c617580260d55310a-medium.jpg5ea10e2c617580260d55310a2['Summer', 'cardigan', 'women beachwear', 'chiffon', 'Sexy women', 'Coat', 'summercardigan', 'openfront', 'short sleeves', 'Swimsuit', "Women's Fashion", 'leaf', 'Green', 'printed', 'Spring', 'longcardigan', 'Women', 'Beach', 'kimono']
3Hot Summer Cool T-shirt pour les femmes Mode Tops Abeille Lettres imprimées Manches courtes O Neck Coton T-shirts Tops Tee VêtementsHot Summer Cool T Shirt for Women Fashion Tops Bee Printed Letters Short Sleeve O Neck Cotton T-shirts Tops Tee Clothing8.008500014.03579295.0119.0119.042.036.00000Summer,Shorts,Cotton,Cotton T Shirt,Sleeve,printedletterstop,Clothing,Tops,Necks,short sleeves,Women's Fashion,Women Clothing,printed,Women,tshirtforwomen,Fashion,T Shirts,ShirtblackM502041500CNallenfanallenfan(23,832 notes)238324.02043558cfdefdacb37b556efdff7c0https://www.wish.com/c/5cedf17ad1d44c52c59e4acahttps://contestimg.wish.com/api/webimage/5cedf17ad1d44c52c59e4aca-medium.jpg5cedf17ad1d44c52c59e4aca4['Summer', 'Shorts', 'Cotton', 'Cotton T Shirt', 'Sleeve', 'printedletterstop', 'Clothing', 'Tops', 'Necks', 'short sleeves', "Women's Fashion", 'Women Clothing', 'printed', 'Women', 'tshirtforwomen', 'Fashion', 'T Shirts', 'Shirt']
4Femmes Shorts d'été à lacets taille élastique lâche mince pantalon décontracté, plus la taille S-8XLWomen Summer Shorts Lace Up Elastic Waistband Loose Thin Casual Pants Plus Size S-8XL2.72310013.10206.04.04.02.06.00000Summer,Plus Size,Lace,Casual pants,Bottom,pants,Loose,Women's Fashion,Shorts,Lace Up,Elastic,Casual,WomenyellowS11035501CNyoungpeopleshophappyhorses85 % avis positifs (14,482 notes)144824.0015885ab3b592c3911a095ad5dadb0https://www.wish.com/c/5ebf5819ebac372b070b0e70https://contestimg.wish.com/api/webimage/5ebf5819ebac372b070b0e70-medium.jpg5ebf5819ebac372b070b0e703['Summer', 'Plus Size', 'Lace', 'Casual pants', 'Bottom', 'pants', 'Loose', "Women's Fashion", 'Shorts', 'Lace Up', 'Elastic', 'Casual', 'Women']
5Plus la taille d'été femmes décontracté sans manches barboteuses combinaisons combinaison de couleur unie jarretelles pantalons lâche salopettePlus Size Summer Women Casual Sleeveless Rompers Jumpsuits Solid Color Suspender Ttrousers Loose Overalls3.9291005.0011.00.00.00.00.00000Deep V-Neck,Summer,Plus Size,Spaghetti Strap,Overalls,Women's Fashion,sleeveless,Women,Casual,jumpsuitnavyblueSize-XS11040500CNzhoulinglingazhoulinglinga75 % avis positifs (65 notes)653.5076925e4b9c3801ba9d210036fc5a0https://www.wish.com/c/5ec645bafd107a02279c8c54https://contestimg.wish.com/api/webimage/5ec645bafd107a02279c8c54-medium.jpg5ec645bafd107a02279c8c541['Deep V-Neck', 'Summer', 'Plus Size', 'Spaghetti Strap', 'Overalls', "Women's Fashion", 'sleeveless', 'Women', 'Casual', 'jumpsuit']
6Women Fashion Loose Lace Blouse Blouse V Neck Bat Sleeves T Shirt Hollow Out Tops Plus Grande Taille XS-8XLWomen Fashion Loose Lace Blouse V Neck Bat Sleeves T Shirt Hollow Out Tops Plus Size XS-8XL7.0065000003.8467423172.01352.01352.0490.0757.00000blouse,Women,lace t shirt,summer t-shirts,Lace,Sleeve,Women Blouse,loose shirt,Short Sleeve Blouses,Pure Color,Womens Blouse,Bat,lace shirts,Necks,Women's Fashion,Plus Size,loose t-shirt,Short Sleeve T-Shirt,Fashion,Tops,ShirtwhiteXS502031500CNUnique Li Fashion Shopuniquelifashionshopbb657bfe91d211e598c7063a14dc88b586 % avis positifs (10,194 notes)101944.0765165652f4053a698c76dc9a3f371https://www.wish.com/c/5c63a337d5e2ce4bbb3152cfhttps://contestimg.wish.com/api/webimage/5c63a337d5e2ce4bbb3152cf-medium.jpg5c63a337d5e2ce4bbb3152cf1['blouse', 'Women', 'lace t shirt', 'summer t-shirts', 'Lace', 'Sleeve', 'Women Blouse', 'loose shirt', 'Short Sleeve Blouses', 'Pure Color', 'Womens Blouse', 'Bat', 'lace shirts', 'Necks', "Women's Fashion", 'Plus Size', 'loose t-shirt', 'Short Sleeve T-Shirt', 'Fashion', 'Tops', 'Shirt']
7Robe tunique ample femme Robe d'été Robe en jean Robe chemise en jean Robe droiteWomen's Baggy Tunic Dress Summer Dress Denim Dress Denim Shirt Dress Shift Dress12.0011100003.76286120.056.056.018.031.00000Jeans,Fashion,tunic,Shirt,Summer,Dress,Denim,summer dress,denimjeansdres,short sleeves,casual dresses,Women's Fashion,Tunic dress,minishirtdres,Lines,mididreblueM.5030139500CNSo Bandsoband(342 notes)3423.6812875d45349676befe65691dcfbb0https://www.wish.com/c/5e0ae5ebc2efb76ccf0a3391https://contestimg.wish.com/api/webimage/5e0ae5ebc2efb76ccf0a3391-medium.jpg5e0ae5ebc2efb76ccf0a33911['Jeans', 'Fashion', 'tunic', 'Shirt', 'Summer', 'Dress', 'Denim', 'summer dress', 'denimjeansdres', 'short sleeves', 'casual dresses', "Women's Fashion", 'Tunic dress', 'minishirtdres', 'Lines', 'mididre']
8Robe d'été décontractée à manches courtes pour femmesWomen's Summer Casual Dress Fashion Short Sleeve Slim Dress11.008410013.47156.02.02.01.03.00000slim dress,summer dress,womenshortsleevedre,Sleeve,Summer,Dress,slim,short sleeves,Women's Fashion,Shorts,boho dress,slimfitdre,Fashion,CasualblackM502036501CNchenxiangjunjunchenxiangjunjun82 % avis positifs (330 notes)3303.8030305d42980e8388970d32294ddc0https://www.wish.com/c/5e6f1fb7fe4a5bb4b8bf36e5https://contestimg.wish.com/api/webimage/5e6f1fb7fe4a5bb4b8bf36e5-medium.jpg5e6f1fb7fe4a5bb4b8bf36e51['slim dress', 'summer dress', 'womenshortsleevedre', 'Sleeve', 'Summer', 'Dress', 'slim', 'short sleeves', "Women's Fashion", 'Shorts', 'boho dress', 'slimfitdre', 'Fashion', 'Casual']
9Femmes d'été, plus la taille décontractée lâche col en V à manches courtes imprimé floral Blouse TopsSummer Women Plus Size Casual Loose V Neck Short Sleeve Floral Printed Blouse Tops5.7822500003.60687287.0128.0128.068.0112.00000blouse,Summer,Plus Size,Floral print,Necks,Sleeve,summer shirt,Loose,short sleeves,Casual,T Shirts,Shorts,Fashion,Floral,Women,Women's Fashion,Tops,printedbeigeS502033500CNLuowei clotheluoweiclothe85 % avis positifs (5,534 notes)55343.9998195ba2251b4315d12ebce873fa0https://www.wish.com/c/5ccfaf238a8d535cec2dfb47https://contestimg.wish.com/api/webimage/5ccfaf238a8d535cec2dfb47-medium.jpg5ccfaf238a8d535cec2dfb475['blouse', 'Summer', 'Plus Size', 'Floral print', 'Necks', 'Sleeve', 'summer shirt', 'Loose', 'short sleeves', 'Casual', 'T Shirts', 'Shorts', 'Fashion', 'Floral', 'Women', "Women's Fashion", 'Tops', 'printed']

Last rows

titletitle_origpriceretail_priceunits_solduses_ad_boostsratingrating_countrating_five_countrating_four_countrating_three_countrating_two_countrating_one_countbadges_countbadge_local_productbadge_product_qualitybadge_fast_shippingtagsproduct_colorproduct_variation_size_idproduct_variation_inventoryshipping_option_priceshipping_is_expresscountries_shipped_toinventory_totalhas_urgency_bannerorigin_countrymerchant_titlemerchant_namemerchant_info_subtitlemerchant_rating_countmerchant_ratingmerchant_idmerchant_has_profile_pictureproduct_urlproduct_pictureproduct_idnum_listingstag_list
1529ZANZEA Femmes Été Polka Dot Kaftan Beach Club Party Longue Maxi Dress HOT Long DressZANZEA Women Summer Polka Dot Kaftan Beach Club Party Long Maxi Dress HOT Long Dress15.009210003.517426.015.015.07.011.00000Summer,loosedresse,kaftan,long dress,baggydres,Dress,Polkas,robesforwomen,Women's Fashion,34sleevedres,party,roundneckdres,vestido,maxi dress,Beach,polka dot,WomenredL504038500CNfashionforgirlsguangzhouchanny88% Feedback positivo (151,914 classificações)1519144.12792153aa664438d3046ee44a50240https://www.wish.com/c/5da04c1f5949a226113006f1https://contestimg.wish.com/api/webimage/5da04c1f5949a226113006f1-medium.jpg5da04c1f5949a226113006f14['Summer', 'loosedresse', 'kaftan', 'long dress', 'baggydres', 'Dress', 'Polkas', 'robesforwomen', "Women's Fashion", '34sleevedres', 'party', 'roundneckdres', 'vestido', 'maxi dress', 'Beach', 'polka dot', 'Women']
15302018 Femme mode d'été en dentelle Patchwork Patchwork Débardeurs Débardeurs Casual Sleeveless Tops Gilet Chemisier (S-5XL) Grande taille2018 Women fashion Summer Lace Patchwork Tank Tops Casual Sleeveless Tops Vest Blouse (S-5XL) Plus Size5.915100003.38414156.058.058.045.083.00000blouse,Summer,Vest,Fashion,Women Blouse,sleevelessblouse,summer shirt,Loose,tank top,Casual,summerblouse,Women's Fashion,sleeveless tops,women top,casualblouse,WomenWhiteS502036501CNliminnyliminny81 % avis positifs (12,134 notes)121343.86690358aec90823ef726994a323fe0https://www.wish.com/c/5b4ed29514f0765a8a844592https://contestimg.wish.com/api/webimage/5b4ed29514f0765a8a844592-medium.jpg5b4ed29514f0765a8a8445922['blouse', 'Summer', 'Vest', 'Fashion', 'Women Blouse', 'sleevelessblouse', 'summer shirt', 'Loose', 'tank top', 'Casual', 'summerblouse', "Women's Fashion", 'sleeveless tops', 'women top', 'casualblouse', 'Women']
1531Nouveau Pantalon De Mode D'été Femmes Leggings Pantalon Déchiré Pantalon Mince Armée Vert Collants PantalonNew Summer Fashion Trousers Women Leggings Ripped Pants Slim Pants Army Green Tights Pants3.00810013.795725.010.010.03.06.00000Summer,Leggings,Fashion,high waist,pants,slim,Women's Fashion,trousers,Green,Army,WomenskyblueXS11041501CNbujizhanbujizhan(4,080 notes)40803.987990584a7a381591451e4e3af3df0https://www.wish.com/c/5e8f0165e815903d022a3c7chttps://contestimg.wish.com/api/webimage/5e8f0165e815903d022a3c7c-medium.jpg5e8f0165e815903d022a3c7c3['Summer', 'Leggings', 'Fashion', 'high waist', 'pants', 'slim', "Women's Fashion", 'trousers', 'Green', 'Army', 'Women']
1532Robe mi-longue d'été à manches courtes pour femmes Baggy Robes pour femmes Shift Kaftan S-5XLWomens Short Sleeve Baggy Summer Beach Midi Dress Ladies Shift Kaftan Dresses S-5XL11.0013410013.54287.011.011.02.04.00000Summer,Shift Dress,Sleeve,shirt dress,long dress,Beach,Dress,short sleeves,beach dress,Shorts,Midi Dress,Ladies,Women's Fashion,loose dress,kaftandreblackS503046500CNSCOMELYscomely86 % avis positifs (1,926 notes)19264.071651593402ae25c4f54ed4e0abdf0https://www.wish.com/c/5d1060d39ed281190dfcec91https://contestimg.wish.com/api/webimage/5d1060d39ed281190dfcec91-medium.jpg5d1060d39ed281190dfcec912['Summer', 'Shift Dress', 'Sleeve', 'shirt dress', 'long dress', 'Beach', 'Dress', 'short sleeves', 'beach dress', 'Shorts', 'Midi Dress', 'Ladies', "Women's Fashion", 'loose dress', 'kaftandre']
1533Combinaison sans manches pour femmes couleur unie Dames Slim Short Bodycon Rompers Femmes BodySleeveless Solid Color Women Jumpsuit Ladies Slim Short Bodycon Rompers Women Bodysuit8.0072000014.2531271919.0580.0580.0128.0196.01010bodycon jumpsuits,nightwear,Shorts,slim,Body Suit,shortjumpsuit,Women,vestido,Ladies,sleeveless,sexy,Rompers,Casual,jumpsuitblackM502044500CNRell Mailrellmail88 % avis positifs (16,803 notes)168034.15503256455b13b15aab129db58cb70https://www.wish.com/c/5c91a7ae7cfe8e4e64c36d97https://contestimg.wish.com/api/webimage/5c91a7ae7cfe8e4e64c36d97-medium.jpg5c91a7ae7cfe8e4e64c36d973['bodycon jumpsuits', 'nightwear', 'Shorts', 'slim', 'Body Suit', 'shortjumpsuit', 'Women', 'vestido', 'Ladies', 'sleeveless', 'sexy', 'Rompers', 'Casual', 'jumpsuit']
1534Nouvelle Mode Femmes Bohême Pissenlit Imprimer Tee Shirt Lady Fille T-shirt À Manches Courtes Boho Graphique Tee Casual Yoga Top Plus La TailleNew Fashion Women Bohemia Dandelion Print Tee Shirt Lady Girl Short Sleeve T-shirt Boho Graphic Tee Casual Yoga Top Plus Size6.0091000014.081367722.0293.0293.077.090.00000bohemia,Plus Size,dandelionfloralprinted,short sleeves,yoga top,bohotshirt,Cool T-Shirts,Women's Fashion,Fashion,short sleeve shirt,Casual,Women,Shorts,Yoga,Shirt,Sleeve,graphic tee,Tee Shirt,T Shirts,boho,bohoshirt,Print,Casual Tops,TopsnavyblueS502041500CNcxuelin99126cxuelin9912690 % avis positifs (5,316 notes)53164.2246055b507899ab577736508a07820https://www.wish.com/c/5d5fadc99febd9356cbc52eehttps://contestimg.wish.com/api/webimage/5d5fadc99febd9356cbc52ee-medium.jpg5d5fadc99febd9356cbc52ee3['bohemia', 'Plus Size', 'dandelionfloralprinted', 'short sleeves', 'yoga top', 'bohotshirt', 'Cool T-Shirts', "Women's Fashion", 'Fashion', 'short sleeve shirt', 'Casual', 'Women', 'Shorts', 'Yoga', 'Shirt', 'Sleeve', 'graphic tee', 'Tee Shirt', 'T Shirts', 'boho', 'bohoshirt', 'Print', 'Casual Tops', 'Tops']
153510 couleurs femmes shorts d'été lacent ceinture élastique culotte lâche, plus la taille S-6XL10 Color Women Summer Shorts Lace Up Elastic Waistband Loose Panties Plus Size S-6XL2.005610013.072811.03.03.03.010.00000Summer,Panties,Elastic,Lace,Casual pants,casualshort,summer shorts,Plus Size,Short pants,women shorts,Shorts,Beach Shorts Women,Beach Shorts,loosepant,high waisted shorts,Lace Up,Women's Fashion,WomenlightblueS21026501CNsell best quality goodssellbestqualitygoods(4,435 notes)44353.69605454d83b6b6b8a771e478558de0https://www.wish.com/c/5eccd22b4497b86fd48f16b4https://contestimg.wish.com/api/webimage/5eccd22b4497b86fd48f16b4-medium.jpg5eccd22b4497b86fd48f16b43['Summer', 'Panties', 'Elastic', 'Lace', 'Casual pants', 'casualshort', 'summer shorts', 'Plus Size', 'Short pants', 'women shorts', 'Shorts', 'Beach Shorts Women', 'Beach Shorts', 'loosepant', 'high waisted shorts', 'Lace Up', "Women's Fashion", 'Women']
1536Nouveautés Hommes Siwmwear Beach-Shorts Hommes Summer Short de bain court à séchage rapide Beach-Wear SportsNew Men Siwmwear Beach-Shorts Men Summer Quick-Dry Short Swim-Shorts Beach-Wear Sports5.001910003.715924.015.015.03.09.00000runningshort,Beach Shorts,beachpant,menbeachshort,Men,sailboatshort,beach swimwear,Men's Fashion,Shorts,Summer,men's shorts,SportwhiteSIZE S152011500CNshixueyingshixueying86 % avis positifs (210 notes)2103.9619055b42da1bf64320209fc8da690https://www.wish.com/c/5e74be96034d613d42b52dfehttps://contestimg.wish.com/api/webimage/5e74be96034d613d42b52dfe-medium.jpg5e74be96034d613d42b52dfe1['runningshort', 'Beach Shorts', 'beachpant', 'menbeachshort', 'Men', 'sailboatshort', 'beach swimwear', "Men's Fashion", 'Shorts', 'Summer', "men's shorts", 'Sport']
1537Mode femmes d'été sans manches robes col en V dos nu robe en dentelle dames robes de plage robe blancheFashion Women Summer Sleeveless Dresses V Neck Backless Lace Dress Ladies Beach Dresses White Dress13.001110002.5020.01.01.00.01.00000Summer,fashion women,Fashion,Lace,Dresses,Dress,Lace Dress,Women's Fashion,ladies dress,beach dress,Sleeveless dress,backless,women's dress,sleeveless,Ladies,women dress,V-neck Dresses,Women,Beach,white,NeckswhiteSize S.363029500CNmodaimodai77 % avis positifs (31 notes)313.7741945d56b32c40defd78043d5af90https://www.wish.com/c/5eda07ab0e295c2097c36590https://contestimg.wish.com/api/webimage/5eda07ab0e295c2097c36590-medium.jpg5eda07ab0e295c2097c365902['Summer', 'fashion women', 'Fashion', 'Lace', 'Dresses', 'Dress', 'Lace Dress', "Women's Fashion", 'ladies dress', 'beach dress', 'Sleeveless dress', 'backless', "women's dress", 'sleeveless', 'Ladies', 'women dress', 'V-neck Dresses', 'Women', 'Beach', 'white', 'Necks']
1538Pantalon de yoga pour femmes à la mode Slim Fit Fitness Running LeggingsFashion Women Yoga Pants Slim Fit Fitness Running Leggings7.00610014.07148.03.03.00.02.00000Summer,Leggings,slim,Yoga,pants,Slim Fit,Women's Fashion,Running,Fashion,Sport,Fitness,WomenredS502041500CNAISHOPPINGMALLaishoppingmall90 % avis positifs (7,023 notes)70234.2359395a409cf87b584e7951b2e25f0https://www.wish.com/c/5e857321f53c3d2d8f25e7edhttps://contestimg.wish.com/api/webimage/5e857321f53c3d2d8f25e7ed-medium.jpg5e857321f53c3d2d8f25e7ed1['Summer', 'Leggings', 'slim', 'Yoga', 'pants', 'Slim Fit', "Women's Fashion", 'Running', 'Fashion', 'Sport', 'Fitness', 'Women']